Siraj Raval: Recent Episodes

Siraj Raval

I'm Siraj. I'm on a warpath to inspire and educate developers to build Artificial Intelligence. Games, music, chatbots, art, i'll teach you how to make it all yourself.

We are the fastest growing AI community in the world.

Our mission: Solve AI. Use it to benefit humanity.

I've been called

Bill Nye of Computer Science Kanye of Code Beyonce of Neural Networks Osain Bolt of Learning Chuck Norris of Python Jesus Christ of Machine Learning

but it's the other way. They are the Siraj Raval of X

I do this alone. All content was by me. And the writing will always be my own, I will never delegate that. Every word I speak comes from my soul. It's real.

Follow me on Twitter and Facebook!

Twitter: http://www.twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology/?fref=ts

If you found my videos useful, I'd love your support on Patreon :) https://www.patreon.com/user?ty=h&u=3191693

In the event of my demise, you must finish what I've started here.

Thanks for watching!

I've built an app called VectorFunnel that automatically scores leads for marketing & sales teams! I used React for the frontend, Node.js for the backend, PostgreSQL for the database, and Tensorflow.js for scoring each lead in an excel spreadsheet. There are a host of other tools that I used like ClearBit's data API and various Javascript frameworks. If you have no idea what any of that is, that's ok I'll show you! In this video, I'll explain how I built the app so that you can understand how all these parts fit together. The learning goal here is to give you enough of an idea of how these tools work to be able to formulate a plan for your own marketing startup MVP (minimum viable product). Enjoy!

Code for this video: https://github.com/llSourcell/Watch_Me_Build_a_Marketing_Startup

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Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval

I also have an ongoing challenge for any Wizard brave enough to start their own startup. See details + rewards here: https://github.com/llSourcell/Build-an-AI-Startup-with-PyTorch/blob/master/README.md

Make Money with Tensorflow 2.0: https://youtu.be/WS9Nckd2kq0

How to Make Money with Tensorflow: https://www.youtube.com/watch?v=HhqhFbwiaig

7 Ways to Make Money with Machine Learning: https://www.youtube.com/watch?v=mrRfpiAwad0&t=2s

Watch me Build an AI Startup: https://www.youtube.com/watch?v=NzmoPqte4V4&t=1823s

Intro to Tensorflow: https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams!

Join us at the School of AI: https://theschool.ai/

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And please support me on Patreon: https://www.patreon.com/user?u=3191693

What does the field of Artificial Intelligence look like in 2040? It's a really hard question to answer since there are still so many unanswered questions about the nature of reality and computing. In this episode, I'll make my best predictions about AI hardware, AI software, and the societal impact of AI in 2040. We'll cover quantum mechanics, neuromorphic computing, DNA storage, decentralized computing, basic income, and mind-body machines. Enjoy!

Code for this video: https://github.com/llSourcell/quantum_machine_learning_LIVE/blob/master/Demo.ipynb

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More learning resources: https://www.youtube.com/watch?v=HhXg6568I3E&t=5s https://www.youtube.com/watch?v=5YxzWnbqaJI https://www.youtube.com/watch?v=WTnxE0wjZaM&t=2s https://www.youtube.com/watch?v=vrdVlMqK5vc https://www.youtube.com/watch?v=e_BOJS1BLj8&t=943s https://www.youtube.com/watch?v=DmzWsvb-Un4 https://www.youtube.com/watch?v=bSw-wcB6GZw&t=1s https://www.youtube.com/watch?v=LhtnECml-KI https://www.youtube.com/watch?v=AAO4oq2M_48 https://www.youtube.com/watch?v=Ewf_gBWBH2A

Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

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I've built an app called NeuralFund that uses Tensorflow 2.0 to make automated investment decisions. I used Tensorflow 2.0 to train a transformer network on time series data that i downloaded using the Yahoo Finance API. Then, I used Tensorflow Serving + Flask to create a simple web app around it. I'll explain what the important parts you should know in Tensorflow 2.0 are, then I'll guide you through my code & thought process of building an AI startup using it. Enjoy!

Code for this video: https://github.com/llSourcell/Make_Money_with_Tensorflow_2.0

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How to Make Money with Tensorflow: https://www.youtube.com/watch?v=HhqhFbwiaig

7 Ways to Make Money with Machine Learning: https://www.youtube.com/watch?v=mrRfpiAwad0&t=2s

Watch me Build an AI Startup: https://www.youtube.com/watch?v=NzmoPqte4V4&t=1823s

Intro to Tensorflow: https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams!

Join us at the School of AI: https://theschool.ai/

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And please support me on Patreon: https://www.patreon.com/user?u=3191693

I've built an automated therapist app called MindRelaxr using PyTorch and a host of other tools (Dialogflow, Tensorflow Lite, Firebase, ONNX, Paypal, and Android Studio). I'm going to show you how I integrated these tools together to build a paid service that uses AI generated Cognitive Behavioral Therapy techniques to help people reduce their depression and anxiety. This app uses a sentiment analysis model trained in PyTorch as well as Google's cloud natural language processing service 'dialogflow' to provide low cost therapy. Enjoy!

Code for this video (includes the #AIStartupChallenge details): https://github.com/llSourcell/Build-an-AI-Startup-with-PyTorch

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Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval

7 Ways to Make Money with Machine Learning: https://www.youtube.com/watch?v=mrRfpiAwad0&t=1s

Start an AI Startup: https://www.youtube.com/watch?v=NzmoPqte4V4&t=1820s

Write a Research Paper: https://www.youtube.com/watch?v=S47RIVkr978&t=2s

How to Teach AI: https://www.youtube.com/watch?v=tczjZOLVjJM

Interview Preparation: https://www.youtube.com/watch?v=5KB5KAak6tM&t=1s https://www.youtube.com/watch?v=OHhoLhYW2cg&t=9s https://www.youtube.com/watch?v=nMK94JlKRb4&t=2s

Programming Competitions: https://www.youtube.com/watch?v=TffGdSsWKlA

Automated Trading: https://www.youtube.com/watch?v=F2f98pNj99k&t=6s https://www.youtube.com/watch?v=ftMq5ps503w&t=6s https://www.youtube.com/watch?v=05NqKJ0v7EE&t=2s

Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams!

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Algorithms govern so much of our lives and dating is no exception! In this video, I frame dating as a data science pipeline and demo how to use AI algorithms to help facilitate discovery, first impressions, and even intimacy. I'll also explain how collaborative filtering and text generation are being used today to match people together with code examples. I hope this gives you a deeper insight into the role technology is currently playing in human relationships. Enjoy!

Code for this video: https://github.com/llSourcell/AI_for_Dating

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More Learning resources: https://www.youtube.com/watch?v=XdM6ER7zTLk&t=774s https://www.youtube.com/watch?v=9gBC9R-msAk http://www.cs.cmu.edu/~ribeiro/pdf/Tu_Ribeiro_Towsley_TR2014.pdf http://web.cs.ucla.edu/~yzsun/papers/snam2016.pdf https://aimm.online/ https://mashable.com/article/future-online-dating/#ua2Paxq2eZqp

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I'm going to build a medical imaging classification app called SmartMedScan! The potential customers for this app are medical professionals that need to scale and improve the accuracy of their diagnoses using AI. From ideation, to logo design, to integrating features like payments and AI into a single app, I'll show you my 10 step process. I hope that by seeing my thought process and getting familiar with the sequence of steps I'll demonstrate,, you too will be as inspired as I am to use this technology to do something great for the world. Enjoy!

Code for this video: https://github.com/llSourcell/AI_Startup_Prototype

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More Learning resources: https://www.youtube.com/watch?v=FTr3n7uBIuE https://www.youtube.com/watch?v=9bbS-trc8ys&t=608s https://www.youtube.com/watch?v=mrRfpiAwad0 https://www.youtube.com/watch?v=HhqhFbwiaig&t=563s

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Natural Language Processing is a field of Artificial Intelligence dedicated to enabling computers to understand and communicate in human language. NLP is only a few decades old, but we've made significant progress in that time. I'll cover how its changed over the years, then show you how you can easily build an NLP app that can either classify or summarize text. This is incredibly powerful technology that anyone can freely use, I'll show you how to do it. Enjoy!

Code for this video: https://github.com/llSourcell/bert-as-service

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More Learning resources: https://www.youtube.com/watch?v=0n95f-eqZdw http://mlexplained.com/2019/01/30/an-in-depth-tutorial-to-allennlp-from-basics-to-elmo-and-bert/ https://towardsdatascience.com/beyond-word-embeddings-part-2-word-vectors-nlp-modeling-from-bow-to-bert-4ebd4711d0ec https://gluon-nlp.mxnet.io/examples/sentence_embedding/bert.html

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Nvidia is the inventor of the GPU! This tech company based in Silicon Valley has played a huge role in the deep learning revolution (which has relied primarily on GPUs for computing), and its transformed many industries. In this video, I interview Bryan Catanzaro, the Vice President of Applied Deep Learning Research at NVIDIA. Bryan got his PhD in AI from Berkeley, invented a language called Copperhead, and is an expert in parallel programming theory. Nvidia invited me to their annual conference, so I took the opportunity to ask Bryan 67 questions while walking through the halls. Enjoy!

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More Learning resources: https://ctnzr.io/ https://developer.nvidia.com/deep-learning https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 https://www.youtube.com/watch?v=Cr6VqTRO1v0&t=342s

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Discrete Math is a subject everyone interested in Computer Science needs to understand. It consists of math branches like graph theory, set theory, number theory, & combinatorics. It helps create databases, algorithms, & security structures. In this video, I'll explain the most relevant topics in Discrete Math one by one as we try to decrypt the password for a SQL database. Along the way, we'll use discrete math in various ways. I wanted to see if I could summarize an important course I took in college in a single video. Enjoy!

Code for this video: https://github.com/llSourcell/DiscreteMath

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More learning resources: http://discrete.openmathbooks.org/home.php https://cse.buffalo.edu/~rapaport/191/S09/whatisdiscmath.html https://www.cs.odu.edu/~toida/nerzic/content/intro2discrete/intro2discrete.html https://www.youtube.com/watch?v=YzfdL58virc

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I recently flew to Singapore to give a keynote on AI in healthcare and attend a panel discussion on the same topic hosted by School of AI and sponsored by Accenture. School of AI is a nonprofit with a goal of giving everyone on Earth a world-class AI education for free. This was a launch event for our first global hackathon called Health Hack that will be hosted in over 30 cities around the world by our Deans (community representatives). In this video, you'll learn about automated diagnostics, synthetic biology, drug discovery, and the ethical implications of AI. Enjoy!

Link to the hackathon (RSVP at the city closest to you if there is one): https://www.theschool.ai/hackathon/

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More learning resources: https://www.youtube.com/watch?v=DCcmFXXAHf4&t=248s https://www.youtube.com/watch?v=hY9Bc3mtphs&t=462s https://rockhealth.com/reports/demystifying-ai-and-machine-learning-in-healthcare/ https://healthcare.ai/ https://www.nvidia.com/en-us/deep-learning-ai/industries/healthcare/

Join us at the School of AI: https://theschool.ai/

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Unsupervised learning is the most exciting subfield of machine learning! Finding structure in unstructured data automatically sounds like a dream come true, no need to have a label! In this video, I'll demonstrate 2 types of unsupervised learning techniques; k means clustering and principal component analysis. We'll use these techniques on neural data from a patient suffering from seizures to see if we can locate the part of their brain in need of surgery to save their life. You'll laugh, you'll cry, but most importantly, you'll learn. Enjoy!

Code for this video: https://github.com/llSourcell/spike_sorting

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More learning resources: https://blog.algorithmia.com/introduction-to-unsupervised-learning/ http://deeplearning.stanford.edu/tutorial/ https://towardsdatascience.com/unsupervised-learning-with-python-173c51dc7f03 https://medium.com/machine-learning-for-humans/unsupervised-learning-f45587588294

Join us at the School of AI: https://theschool.ai/

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Is it possible to use machine learning without needing to code? The answer is yes! Uber's AI lab recently open sourced python library called Ludwig that they've been using internally for 2 years. The tagline is that it allows anyone to use deep learning without coding. It will require some configuration and unix commands to setup, but I'll show you how in this video. I'll also talk about other code-free tools like Azure ML Studio, DataRobot, and DeepCognition. Enjoy!

Code for this video: https://github.com/llSourcell/ludwig

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More learning resources: https://uber.github.io/ludwig/ https://azure.microsoft.com/en-us/services/machine-learning-studio/ https://www.datarobot.com/ https://deepcognition.ai/

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MachineLearning #SirajRaval

The AI research lab DeepMind created an algorithm that beat a top professional StarCraft 2 player for the first time! This is a huge achievement since this is an incredibly complex game that requires long term planning, game theory, and cooperative play. Their algorithm used a mixture of techniques that in the field of deep reinforcement learning. I'll explain how each of these techniques works, and how they all work together in unison. This is an exciting time for the field. Enjoy!

Code for this video: https://github.com/llSourcell/pysc2

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More learning resources: https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ https://www.vox.com/future-perfect/2019/1/24/18196177/ai-artificial-intelligence-google-deepmind-starcraft-game https://medium.freecodecamp.org/an-intro-to-advantage-actor-critic-methods-lets-play-sonic-the-hedgehog-86d6240171d http://www.dcsc.tudelft.nl/~bdeschutter/pub/rep/10_003.pdf

Join us at the School of AI: https://theschool.ai/

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OpenAI has the entire AI community debating its decision to not release the fully trained version of its powerful new text generator model dubbed GPT-2. I'm going to explain how GPT-2 works using code, math, and animations. We'll discuss its potential applications (both good and bad), ways of preventing misuse, and at the end of the video I'll give my take on whether OpenAI was justified in doing so. The transformer architecture is quickly replacing recurrent networks for sequence learning, and OpenAI's GPT-2 is the latest example of using it at scale. Enjoy!

Code for this video: https://github.com/openai/gpt-2

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More learning resources: https://medium.com/@asierarranz/i-have-created-a-website-to-query-the-gpt-2-openai-model-11dd30e1c8b0 https://blog.openai.com/better-language-models/ http://jalammar.github.io/illustrated-transformer/ https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/#.XHVUts9KiLI

Web Demo of GPT-2: https://www.askskynet.com/

Gradient Descent: https://www.youtube.com/watch?v=XdM6ER7zTLk&t=774s

Fakebox: https://machinebox.io/docs/fakebox

Privacy tools: https://github.com/OpenMined/PySyft/tree/master/examples/tutorials

Join us at the School of AI: https://theschool.ai/

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Data Science coding challenge time! The popular Data Science competition website Kaggle has an ongoing competition to solve the problem of earthquake prediction. Given a dataset of seismographic activity from a laboratory simulation, participants are asked to create a predictive model for earthquakes. In this video, I'll attempt the challenge as a way to teach 3 concepts; the Data Science mindset, Categorical Boosting, and Support Vector Regression models. I'll be coding this using python from start to finish in the online Google colab environment. Enjoy!

Code for this video: https://github.com/llSourcell/Kaggle_Earthquake_challenge

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Join us at the School of AI: https://theschool.ai/

More learning resources: https://www.kaggle.com/c/LANL-Earthquake-Prediction/data https://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/ https://blog.griddynamics.com/xgboost-vs-catboost-vs-lightgbm-which-is-best-for-price-prediction/ https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db https://accio.github.io/machinelearning/2018/05/30/catboost.html http://kernelsvm.tripod.com/ https://www.saedsayad.com/support_vector_machine_reg.htm https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html

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Everyone needs to understand regression! Its a useful data science technique that allows us to understand the relationship between different variables. In this video, we'll play the role of a newly hired data analyst at a genetics company trying to find the relationship between advertising mediums (TV, newspaper, radio) and ticket sales to our newly opened theme park. Along the way, we'll learn about 5 types of regression models (linear, non-linear, multiple, lasso, and ridge). Expect math, code, and layers of explanation. Enjoy!

Code for this video: https://github.com/llSourcell/ISL-Ridge-Lasso

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More learning resources: https://www.youtube.com/watch?v=XdM6ER7zTLk https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/ http://statisticsbyjim.com/regression/choose-linear-nonlinear-regression/ https://hbr.org/2015/11/a-refresher-on-regression-analysis http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-values-and-coefficients

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There are so many different ways to visualize data! We're going to learn about the major types of visualizations (relationships, correlations, comparisons) as well as discuss some of the many powerful visualization tools that are available to us. Google Charts, Tableau, there are a lot! We'll break down the visualization process along many dimensions, revealing the subtleties that can make or break a chart. Using the plotly library. we'll visualize a Medium blog post dataset many different ways. Enjoy!

Code for this video: https://github.com/llSourcell/Data_Visualization

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More learning resources: https://medium.com/@Infogram/18-data-visualization-resources-for-education-and-inspiration-529c6f528983 https://machinelearningmastery.com/data-visualization-methods-in-python/ http://newcoder.io/dataviz/intro/ https://www.kaggle.com/benhamner/python-data-visualizations https://towardsdatascience.com/data-science-with-python-intro-to-data-visualization-and-matplotlib-5f799b7c6d82 https://realpython.com/python-data-visualization-bokeh/

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Statistics is crucial to Data Science! In fact, the phrase Data Science was first used in a Statistics conference title. In this video, I'll cover 3 key concepts from Statistics that every Data Scientist needs to know. Statistical features, probability distributions, and Bayesian statistics will be explained using code, theory, and animations. Our specific application will be finding an optimal credit score for someone using Lending Club's loan data. Expect a musical interlude. Enjoy!

Code for this video: https://github.com/llSourcell/LoanDefault-Prediction

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More learning resources: https://www.kdnuggets.com/2018/12/introduction-statistics-data-science.html https://towardsdatascience.com/the-5-basic-statistics-concepts-data-scientists-need-to-know-2c96740377ae https://www.kdnuggets.com/2017/11/10-statistical-techniques-data-scientists-need-master.html https://www.learndatasci.com/tutorials/data-science-statistics-using-python/ https://towardsdatascience.com/probability-and-statistics-explained-in-the-context-of-deep-learning-ed1509b2eb3f https://www.datascience.com/blog/statistics-data-science-interview https://www.datasciencecentral.com/profiles/blogs/29-statistical-concepts-explained-in-simple-english-part-1

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This past week Kaggle invited me to attend an event they hosted called Kaggle Days in Paris, France. Kaggle Days are a global series of offline events for seasoned data scientists and Kagglers. It was a lot of fun! I met some really interesting people from all over the world and the venue they booked is the biggest startup campus in the world (Station F). In this video, I'll introduce Kaggle Days, interview 5 top data scientists, and conclude with a demo from a top Kaggler on inventory prediction. Enjoy!

Code for this video: https://github.com/llSourcell/invent-prediction

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More learning resources: https://kaggledays.com/ https://www.youtube.com/watch?v=suRd3UzdBeo https://www.youtube.com/watch?v=P1lgTATSVYA

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Welcome to Data Lit! This 3 month course is an intro to data science for beginners. In this video, i'll explain how a popular data science technique called sentiment analysis works using a real-world scenario. We'll play the role of a data scientist working at a startup making a personal healthcare device. Using sentiment analysis, we'll understand how consumers feel about a competitors product. That'll help us make decisions on how to promote our own product, and what feature we can focus on the most. Using Python, Twitter, and Google Colab, anyone can do this process in just a few minutes. Enjoy!

Code for this video: https://github.com/llSourcell/Sentiment_Analysis

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More learning resources: https://towardsdatascience.com/sentiment-analysis-with-python-part-1-5ce197074184 https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python/ https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python https://www.kaggle.com/ngyptr/python-nltk-sentiment-analysis https://pythonspot.com/python-sentiment-analysis/ https://www.analyticsvidhya.com/blog/2018/07/hands-on-sentiment-analysis-dataset-python/

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Which deep learning framework should you use? In this video I'll compare 10 deep learning frameworks across a wide variety of metrics. PyTorch, Tensorflow, MXNet, Chainer, CNTK, Sonnet, DeepLearning4J, CoreML, ONNX, we've got a lot to cover in this video! Using code, programmatic features, and theory, I'll navigate this field ultimately coming to some clear conclusions. Enjoy!

Code for this video: https://github.com/llSourcell/DeepLearningFrameworks

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More learning resources: https://www.netguru.com/blog/deep-learning-frameworks-comparison https://towardsdatascience.com/deep-learning-framework-power-scores-2018-23607ddf297a https://www.exastax.com/deep-learning/a-comparison-of-deep-learning-frameworks/ https://skymind.ai/wiki/comparison-frameworks-dl4j-tensorflow-pytorch https://www.marutitech.com/top-8-deep-learning-frameworks/

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Welcome to AI Humanities! I'm going to review 10 books that I've recently read in this video. I've formed these books into a sequential curriculum that serve as an "AI Humanities" elective class at School of AI. Literature Humanities was one of the few classes at Columbia I enjoyed, and I'm taking the structure that I learned there and evolving it into one dedicated to AI. These books have all helped me immensely in my brainstorming and ideation phases. I hope you find them useful too. Enjoy!

Curriculum for this video: https://github.com/llSourcell/AI_Humanities

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Pre-requisite book: Hands on Machine Learning : https://amzn.to/2FDEH4K

Books: → The Master Algorithm by Pedro Domingos | https://amzn.to/2FDCer0 → Life 3.0 by Max Tegmark | https://amzn.to/2RZH0ER → Superintellgence by Nick Bostrom | https://amzn.to/2CwiscR → Beyond Good and Evil by Fredrich Nietzsche | https://amzn.to/2DpvxGy → 12 Rules for Life by Jordan Peterson | https://amzn.to/2U7gHd9 → 10 Reasons to Delete Your Social Media by Jaron Lanier | https://amzn.to/2MsHzC9 → The Gene by Siddharth Mukerjee | https://amzn.to/2DokWf9 → The Physics of God by Joseph Selbie | https://amzn.to/2CBAsCL → AI Superpowers by Kai-Fu Lee https://amzn.to/2T3C1Ad → The 3 Body Problem by Cixin Lee https://amzn.to/2T0CBie

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Ten Arguments for Deleting Your Social Media Accounts Right Now

The Three-Body Problem (The Three-Body Problem Series, 1)

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Life 3.0: Being Human in the Age of Artificial Intelligence

Superintelligence: Paths, Dangers, Strategies

The Physics of God: Unifying Quantum Physics, Consciousness, M-Theory, Heaven, Neuroscience and Transcendence

Ai Superpowers: China, Silicon Valley, and the New World Order

Beyond Good and Evil

12 Rules for Life: An Antidote to Chaos

THE GENE: An Intimate History

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Make the data lit! This lyrics of this music video are actually educational and they serve as an introductory lecture on AI. This video also acts as a teaser trailer for my upcoming, free 3 month Data Science course for beginners titled "Data Lit" at School of AI (Jan 28 start date). 2019 is our year to shine Wizards, I hope my song gets you as hype as I am to make a huge impact in the world using the power of AI and data. Enjoy!

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Lyrics: School of AI 2019! Shoutout to my Wizards, this ones for you

It started with hello world hello engineers And now we’re , world-wide yo and this the premiere So u gotta sit down tight and let me teach you a lesson I call it intro to AI, this is my confession Lesson one starts simple gotta get that data Don’t even mess with the thetas until we get that data And if we open the file, it might look like a haze, But if we keep it algorithmic we can set it ablaze Hello! Algebra ops its input times weight Its a Matrix of nums, add a bias, activate And when it learns from the data it’ll tell us things Like how to cure a disease or how to spend our money Or even, generate a game you won’t believe, Its like build train test done, NLP Damn! Bad drivers let me show you my friend Cause you can drive it hands free and still have so much to spend

(repeat 2x) Make the Data Lit, Make the Data Lit Make the Data, Make the Data Lit, Make the Data Lit, Learn from it Make the Data Lit, Make the Data Lit, Make the Data, Make the Data Lit, Learn from it

From Paris to Mexico, and New York to Seoul We’re called the School of AI and we keep it under control Listen, If you need some support, I got an SVM It can split up the data like its for us and them Models can generate and models can classify Models can allocate, my model is ride or die Numbers keep defining all the rules of the game And all these matrix operations got my feeling insane Yeah, Turn it up, go, accel-le-erate And if we go tryna quantum boost then its 10x the rate You can hyper-learn it all if theres a will a way Just bare in mind ,if you over-fit you’ll break your display

I train my bot when Im feeling low, I train my bot till it paints Van Gogh I train my bot to help visualize, I train my bot till it harmonize

(repeat 2x) Make the Data Lit, Make the Data Lit Make the Data, Make the Data Lit, Make the Data Lit, Learn from it Make the Data Lit, Make the Data Lit, Make the Data, Make the Data Lit, Learn from it

The games called called evolution. We use technology to evolve. Light the way Wizards, it starts with us.

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DataLit #SchoolOfAI #SirajRaval

DeepMind, an AI lab & complete outsider to the field of molecular biology, beat top pharmaceutical companies with 100K+ employees like Pfizer, Novartis, etc. at predicting protein structures. This is huge! DeepMind didn't yet release the paper or code, so I browsed the CASP reports and different related papers to reverse engineer the architecture as best as I could. What I ended up doing is forking a related research papers code and repurposing it, since its very similar (2 residual networks were used). In this episode, i explain the different components of its architecture, why the protein folding problem is so important, and give some programmatic examples. Enjoy!

Code for this video (with coding challenge): https://github.com/llSourcell/DeepFolding

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More learning resources: https://deepmind.com/blog/alphafold/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://www.reddit.com/r/MachineLearning/comments/a2oaiy/r_alphafold_using_ai_for_scientific_discovery/ https://medium.com/syncedreview/deepmind-alphafold-delivers-unprecedented-progress-on-protein-folding-789fcc1420ea https://news.ycombinator.com/item?id=18587612

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This won the best paper award at NeurIPS (the biggest AI conference of the year) out of over 4800 other research papers! Neural Ordinary Differential Equations is the official name of the paper and in it the authors introduce a new type of neural network. This new network doesn't have any layers! Its framed as a differential equation, which allows us to use differential equation solvers on it to approximate the underlying function of time series data. Its very cool and will ultimately allow us to learn from irregular time series datasets more efficiently, which applies to many different industries. I'll cover all the prerequisites in this video and point to helpful resources down below. Enjoy!

Code for this video: https://github.com/llSourcell/Neural_Differential_Equations/

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More learning resources: https://www.youtube.com/watch?v=q555kfIFUCM&lc=z23fvvbypqe3jzrc2acdp432igdvd11cmfijivtyfvxw03c010c https://www.youtube.com/watch?v=h3l4qz76JhQ&t=9s https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr https://towardsdatascience.com/paper-summary-neural-ordinary-differential-equations-37c4e52df128 https://arxiv.org/abs/1806.07366 https://blog.acolyer.org/2019/01/09/neural-ordinary-differential-equations/ https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/

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Kubernetes is one of the highest velocity open source projects in history. Its a tool that enables developers to manage 'containerized' apps in the cloud easily. In this tutorial video, I'll deploy an image classifier app built in python to the cloud using Kubernetes. It's a 3 step process, and along the way I'll explain key concepts surrounding Docker, Google Cloud, and scalability. Enjoy!

Code for this video: https://github.com/llSourcell/kubernetes

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More learning resources: https://www.youtube.com/watch?v=FTr3n7uBIuE https://www.youtube.com/watch?v=cAICT4Al5Ow https://www.youtube.com/watch?v=VKoLGnq15RM https://www.youtube.com/watch?v=BlPFE7R8psk https://blog.containership.io/k8svsdocker/ https://medium.com/analytics-vidhya/deploy-your-first-deep-learning-model-on-kubernetes-with-python-keras-flask-and-docker-575dc07d9e76

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2018 has been an eventful year for AI to say the least! We've seen advances in generative models, the AlphaGo victory, several data breach scandals, and so much more. I'm going to briefly review AI in 2018 before giving 10 predictions on where the space is going in 2019. Prepare yourself, my predictions range from more Kubernetes infused ML pipelines to the first business use case of generative modeling of 3D worlds. Happy New Year and enjoy!

Code for this video: https://github.com/llSourcell/kubernetes

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More learning resources: https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV https://www.youtube.com/watch?v=i_McNBDP9Qs&list=PL2-dafEMk2A5FZ-MnPMpp3PBtZcINKwLA https://www.youtube.com/watch?v=mrRfpiAwad0&t=327s

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The blockchain space has seen its fair share of issues in 2018. This technology enabled anyone, anywhere to create their own market programmatically. The result was incredible innovation, many high profile scams, and a lot of hard lessons on what doesn't work. I authored a book on this space because I believed in the potential for this technology and still do. I'm going to share my vision for 2019 using the lessons learned from 2018, then demonstrate some businesses who've provided real value to people using this technology. Enjoy!

Code for this video: https://github.com/llSourcell/pyquarkchain

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More learning resources: https://www.theschool.ai/courses/decentralized-application/ https://medium.com/prysmatic-labs/how-to-scale-ethereum-sharding-explained-ba2e283b7fce https://www.youtube.com/watch?v=gSQXq2_j-mw https://www.youtube.com/watch?v=-_Qs0XdPpw8 https://www.youtube.com/watch?v=GWOdAAFoSFE https://www.youtube.com/watch?v=vVsIHCTGjsE https://www.youtube.com/watch?v=LZEHOlZY2To https://www.youtube.com/watch?v=aPVmd7SyKfQ

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Data Science is becoming more and more popular as a career choice since it offers both lucrative salaries and the opportunity to have high impact. The Data Science interview process is challenging, but with dedicated practice you can succeed. In this video, I'll outline the 7 steps to pass any Data Science Interview. We'll go over topics like studying techniques, portfolio optimization, and interviewing tips, all of which are prominent in the modern Data Science interview pipeline. I've listed all of the resources I've mentioned both in the video description and in the associated GitHub readme. Enjoy!

Plan for this video: https://github.com/llSourcell/Data_Science_Interview_Guide

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Step 1 - Ask yourself "Why"

Investor Insights for Inspiration https://thesyndicate.vc/the-top-5-startup-technology-angel-investing-and-venture-capital-podcasts-in-order/

Investors to follow on Twitter http://www.venturearchetypes.com/faq/Investors-On-Twitter-List.html

Data Stories Podcasts http://datastori.es/

Step 2 - Create and Execute a Study Plan

Learn Data Science in 3 Months https://www.youtube.com/watch?v=9rDhY1P3YLA

Chromebook Data Science https://leanpub.com/universities/set/jhu/chromebook-data-science

Open Source University https://github.com/ossu/data-science

Practice Technical Interviews http://interviewing.io

Use Flashcards https://www.brainscape.com/subjects/data-science

Find a Mentor https://www.sharpestminds.com

Step 3 - Build a Portfolio

Design a resume + personal website https://www.youtube.com/watch?v=nMK94JlKRb4

And have 3 projects on Github, one should have a web presence

Step 4 - Start Pitching for Jobs

https://www.angel.co
Ask friends on Social Media
https://www.ventureloop.com/ventureloop/job_search.php (VC Portfolio companies job listings)
https://news.ycombinator.com/
Find recruiters using "site:linkedin.com quora technical recruiter" but replace quora with your company

Scheduling tool https://calendly.com/sirajraval/

Step 5 - Complete the Interview

Study Data Science Interview Questions on Glassdoor https://www.glassdoor.com/Interview/data-scientist-interview-questions-SRCH_KO0,14.htm

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DataScienceInterview #SirajRaval #MachineLearning

Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well. Enjoy!

Code for this video: https://github.com/llSourcell/pytorch_geometric

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More learning resources: http://sungsoo.github.io/2018/02/01/geometric-deep-learning.html http://geometricdeeplearning.com/ https://arxiv.org/abs/1611.08097 http://3ddl.stanford.edu/CVPR17_Tutorial_Intrinsic_CNNs_compressed.pdf https://github.com/rusty1s/pytorch_geometric https://pemami4911.github.io/paper-summaries/deep-learning-theory/2017/11/19/geometric-deep-learning.html

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Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/

Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/

Learning the tools that preserve user privacy is going to become an increasingly important skillset for all aspiring data scientists to learn in the coming months. Legal frameworks like GDPR are being proposed all around the world as people realize how valuable their data is, so data scientists need to accept that they'll have to handle data differently than in the past. In this video, I'll demo 3 important privacy techniques; differential privacy, secure multi party computation, and federated learning. We'll use these techniques to train a mode built with Python l to predict diabetes while keep user data anonymous. Enjoy!

Code for this video: https://github.com/OpenMined/PySyft/tree/master/examples/tutorials

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More learning resources: https://www.openmined.org/ https://iamtrask.github.io/2017/03/17/safe-ai/ https://mortendahl.github.io/ https://florian.github.io/federated-learning/ https://towardsdatascience.com/whats-new-in-deep-learning-research-understanding-federated-learning-b14e7c3c6f89 http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html

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Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/

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We are School of AI! Our mission is to offer a world-class AI education to anyone on Earth for free. Our doors are open to all those who wish to learn. We are a learning community that spans almost every country dedicated to teaching our students how to make a positive impact in the world using AI technology. In this video, I make a request for applications to sign-up to be a School of AI Residential Advisor. Residential Advisors provide quality co-living housing for School of AI Wizards in their respective cities. We’re looking for people who either own, lease, rent, or in some way manage a co-living property with at least 5 beds, but the more the better. The type of property can be a big house, an apartment complex, a hostel, or even an existing college dorm.

Signup here: https://docs.google.com/forms/d/e/1FAIpQLSdctYUsnVwrIPZy6NT-zFcwL-nj_kCq2WDPQoA-FdWILExtIQ/viewform

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More Learning Resources: https://www.youtube.com/watch?v=8yu8rtXThy8&t=4s https://www.youtube.com/watch?v=_7TvMA_w8xw

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This video is sponsored by IBM.

Watson Discovery is an IBM Cloud service which allows you to unlock hidden value in data to find answers, monitor trends, and surface patterns using the world’s most advanced cloud-native insight engine. Watson Discovery ships with natural language processing built-in and can even be taught to understand terms that are specific to your domain. By automating the ingestion and processing of your data in a fully managed cloud service, it removes the complexity from your workflow and allows you to spend less time wrangling your data and more time building!

Ready to get started with Watson Discovery? Sign up for your IBM Cloud Account: https://ibm.biz/ibm-cloud-signup Watson Discovery Documentation: https://ibm.biz/watson-discovery-docs Mining insights from data breaches (TUTORIAL): https://ibm.biz/discovery-insights-tutorial Create a stock information app (TUTORIAL): https://ibm.biz/stock-app-tutorial

Code (+ Challenge) for this video: https://github.com/llSourcell/IBM_Watson_Discovery

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A Chinese scientist recently claimed to have created the first genetically modified human embryos. Using a technology called CRISPR, he made twin baby girls resistant to HIV before they were born. This opened up serious debate across the world on the implications of this technology. I'm going to take this opportunity to give an in-depth analysis of biotechnology, and and answer some really hard questions. Can we really program biology, and if so how? What are the implications of designer babies? Should humans live forever? I really put my heart into this video, I hope you find it useful and inspiring. Enjoy!

Code for this video: https://github.com/llSourcell/Elevation

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More learning resources: https://github.com/topics/synthetic-biology?l=python https://discoverysedge.mayo.edu/2018/07/24/the-gene-editing-tool-crispr-explained/ https://www.vox.com/2018/7/23/17594864/crispr-cas9-gene-editing https://ghr.nlm.nih.gov/primer/genomicresearch/genomeediting https://biopython.org/ https://www.nytimes.com/2018/11/30/world/asia/gene-editing-babies-china.html

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CRISPR #SirajRaval

D-Wave’s Leap service is the first cloud based quantum application environment that gives developers real-time access to a live quantum computer. In this video, we’ll use the Leap service to build a simple application to monitor a crop health sensor network. I've been collaborating with D-Wave on this, and this video will go over several existing use cases, detail how quantum computing uses quantum mechanics, and demo using it for a real-world application. Entanglement, superposition, tunneling, there are some crucial concepts for us to understand to be able to harness quantum technology and through animations, code, and explanations I hope you too get excited about its potentials. Enjoy!

Code examples: https://www.dwavesys.com/

Coding Challenge: Create a simple app using Leap that solves a problem. Submit your github repository to hello@sirajraval.com by December 9th at midnight PST. I'll give the top 2 entries a video shoutout a week after that!

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More learning resources: https://www.youtube.com/watch?v=yB6tnJdttjw https://www.techrepublic.com/article/d-wave-quantum-computers-the-smart-persons-guide/ https://uwaterloo.ca/institute-for-quantum-computing/quantum-computing-101 https://www.youtube.com/watch?v=LhtnECml-KI&vl=en https://www.youtube.com/watch?v=DmzWsvb-Un4

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This video was sponsored by D-Wave

I've laid out the blueprint for a brain computer interface called "The Link". This is a device that uses DNA for processing and storage, temporal interference to alter brain states, and infrared spectroscopy to read brain states. Its powered using your body heat, noninvasive, and waterproof. This device is just a thought experiment, but real companies are working on this technology today. What does this kind of technology entail for privacy? Will there be an intelligence disparity? What role does AI play in all of this? What does it even mean to be human? I tackle these hard questions and give a technical overview of this device in this video. Enjoy!

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Code for this video: https://github.com/llSourcell/the_link

More learning resources: http://kernel.co https://osfund.co/ https://www.sciencedaily.com/news/mind_brain/brain-computer_interfaces/ https://www.sciencedaily.com/news/mind_brain/brain-computer_interfaces/ https://www.bloomberg.com/news/features/2017-09-07/brain-computer-interfaces-are-already-here https://medicalxpress.com/news/2018-10-brain-computer-interface.html https://medium.com/@devdevcharlie/experimenting-with-brain-computer-interfaces-in-javascript-8d6cb891fda8

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The Chinese state run news agency Xinhua recently revealed the first Artificial Intelligence news anchor. They're now able to generate video of a newscaster using a model trained on real newscaster data and use it to disseminate information 24/7. When I saw this, I knew it was time to start studying China's role in the AI revolution in-depth. In this video, I'll cover China's power structure, generative adversarial networks, its startup scene, Confucianism, the social credit scoring system, algorithmic policing, surveillance, privacy, autonomous weapons, and convolutional neural networks. A lot of different topics to cover, but I hope this video provides a coherent narrative around the use of AI in China and how it plays into the global AI community. Enjoy!

Code for this video: https://github.com/llSourcell/Talking-Face-Generation-DAVS

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More learning resources: https://www.npr.org/2018/10/31/662436265/china-tests-a-social-credit-score https://www.theverge.com/2018/11/8/18074806/ai-news-anchor-china-xinhua-digital-composite https://skymind.ai/wiki/generative-adversarial-network-gan https://towardsdatascience.com/build-your-own-convolution-neural-network-in-5-mins-4217c2cf964f https://machinelearningmastery.com/multi-class-classification-tutorial-keras-deep-learning-library/ https://www.youtube.com/watch?v=PhCM3qoRZHE https://www.youtube.com/watch?v=yz6dNf7X7SA

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A team of researchers at Google Brain and Georgia Tech developed an AI that learned how to dress itself using various types of clothing. They demonstrated their technology by presenting a video that shows an animated figure gracefully putting on clothing, and the most interesting part is that it learned how to do this by itself. The technique they used was called Trust Region Policy Optimization and its one of the techniques at the forefront of AI research. I'll admit that this was one of the most difficult papers I've ever read, if not the most difficult. The Differentiable Neural Computer paper would be a close 2nd. Using code, animations, theory, and memes, I'll explain how this algorithm works and demonstrate how we can use it to improve our lives in the areas of gaming, elder-care, and factory automation.

Code for this video: https://github.com/llSourcell/AI_Dresses_Itself

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More learning resources: https://www.cc.gatech.edu/~aclegg3/projects/learning-dress-synthesizing.pdf https://medium.com/@jonathan_hui/rl-trust-region-policy-optimization-trpo-explained-a6ee04eeeee9 http://178.79.149.207/posts/trpo.html https://www.depthfirstlearning.com/2018/TRPO https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-part-ii-trpo-ppo-87f2c5919bb9

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Time series is the fastest growing category of data out there! It's a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I'll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We'll compare the results of each technique, and even consider using a learning technique. From Holts Winter Method to Vector Auto Regression to Reinforcement Learning, we've got a lot to cover here. Enjoy!

Code for this video: https://github.com/llSourcell/Time_Series_Prediction

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More learning resources: https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f https://towardsdatascience.com/bitcoin-price-prediction-using-time-series-forecasting-9f468f7174d3 https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/ https://www.youtube.com/watch?v=hhJIztWR_vo

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Why was AlphaGo's Move 37 against Lee Sedol so significant? Why was it so important that I named my 10 week course on deep reinforcement learning on it? In this final video of my course, I'll explain what move 37 symbolized for humanity and detail 3 examples of how it will affect healthcare, design, and decision-making. We'll go through a code example of a Generative Adversarial Network and even discuss China ambitious 2030 AI initiative. Theres a lot that I cover in this video, I hope that it helps connect the dots. Enjoy!

Code for this video: https://github.com/tensorflow/minigo

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This video is apart of Move 37, my course at School of AI: https://theschool.ai/

More learning resources: https://katbailey.github.io/post/was-alphagos-move-37-inevitable/ https://medium.com/@cristobal_esteban/move-37-a3b500aa75c2 https://disruptionhub.com/disrupted-humanity-move-37/ https://www.youtube.com/watch?v=UzYeqAJ2bA8 https://www.youtube.com/watch?v=hY9Bc3mtphs&t=640s

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Drones are being used in real world applications around the world! They can help detect anomalies in crop yields, provide companies with physical assets a real-time continuous data stream, and help secure locations by giving teams an aerial view. We'll learn how a dron can learn to navigate a novel, complex environment using an advanced reinforcement learning technique called Deep Deterministic Policy Gradients. Its a mixture of policy gradients and actor critic, I'll go through the dependencies before explaining the real algorithm. Enjoy!

Code for this video: https://github.com/llSourcell/Drone_Flight_Controller

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This video is apart of Move 37, my course at School of AI: https://theschool.ai/

Drone 3D Environment: https://rds.theconstructsim.com/signup/

More learning resources: https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287 https://coach.nervanasys.com/algorithms/policy_optimization/ddpg/index.html https://towardsdatascience.com/policy-gradients-in-a-nutshell-8b72f9743c5d https://deepmind.com/research/publications/deterministic-policy-gradient-algorithms/ https://medium.com/@kinwo/solving-continuous-control-environment-using-deep-deterministic-policy-gradient-ddpg-agent-5e94f82f366d https://medium.freecodecamp.org/an-introduction-to-policy-gradients-with-cartpole-and-doom-495b5ef2207f

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Machine Learning is an amazing technology, but how are you supposed to earn a living from it? In this video, I'll break down 7 ways that anyone can earn money from anywhere in the world using machine learning. We''ll start by taking a look at whats called the "AI Value Chain" to learn who is currently making money in machine learning so that we can better chart out where we can contribute to the space. From startups, to competitions, to writing books, we've got a lot to cover in this video. Enjoy!

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More learning resources: https://www.youtube.com/watch?v=9bbS-trc8ys https://www.youtube.com/watch?v=x6GYD5WPda4 https://www.youtube.com/watch?v=CGTn0ceOaOM https://www.youtube.com/watch?v=7vunJlqLZok https://www.youtube.com/watch?v=SSu00IRRraY https://www.youtube.com/watch?v=HhqhFbwiaig https://www.kaggle.com/competitions

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The 7 Ways are 1. Start a Startup 2. Contract Work 3. Job or Internship 4. Write a Book 5. Educational Content 6. Automated Trading Bot 7. Competitions

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I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline.

Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months

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Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU

Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability

Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis

Week 4 - Kaggle Project #1

Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/

Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

Week 8 - Kaggle Project #2

Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2

Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf

Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1

Week 12- Kaggle Project #3

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Curiosity is something that all humans exhibit in some way throughout their lives. Recently, a team at Berkeley published a paper on Curiosity driven learning, and they demonstrated how it helped enable their AI agent to learn how to play the popular game Super Mario Brother very efficiently with the added benefit of curiosity to help Mario explore his options. I'll explain how it works in this video using code, animations, math, and the spoken word. This technology can be used to help make our systems more intelligent, and thus our applications more capable of helping other people. Enjoy!

Code for this video: https://github.com/llSourcell/noreward-rl

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This video is apart of the Move 37 course at School of AI: https://www.theschool.ai

More learning resources: https://pathak22.github.io/large-scale-curiosity/ https://pathak22.github.io/large-scale-curiosity/resources/largeScaleCuriosity2018.pdf https://alumni.berkeley.edu/california-magazine/winter-2017-power/super-curious-mario-teaching-ai-keep-asking-questions https://www.technologyreview.com/s/607886/curiosity-may-be-vital-for-truly-smart-ai/ https://www.youtube.com/watch?v=0Ey02HT_1Ho

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ArtificialCuriosity #SirajRaval #AI

Boston Dynamics released yet another incredible video of its bipedal humanoid robot, this time performing parkour by jumping on a series of boxes. In this video, i'll explain how it works at both a hardware and software level. Their real value lies in the specific type of software they are using, we don't know for sure what it is but we can take some educated guesses based on a combination of whats been revealed so far and what's worked in other humanoid robots. Prepare yourselves for some mechanical engineering and control theory Wizards, enjoy!

Code for this video: https://github.com/llSourcell/Boston_Dynamics_Atlas_Explained

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More learning resources: https://www.bostondynamics.com/atlas https://www.cs.cmu.edu/~cga/drc/door-submitted.pdf http://gazebosim.org/tutorials?tut=drcsim_atlas_robot_interface&branch=issue_24_atlas_robot_interface_drcsim_4 http://www.ent.mrt.ac.lk/~rohan/teaching/EN5001/Reading/DORFCH1.pdf http://engineering.nyu.edu/mechatronics/smart/Archive/intro_to_rob/Intro2Robotics.pdf https://www.youtube.com/watch?v=LikxFZZO2sk

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Meta learning describes the concept of 'learning to learn'. What if we could have AI learn how to optimize itself? An AI could learn the optimal hyper-parameters, architecture, and even dataset! Its a really interesting topic, and in this video I'll describe some meta learning techniques and focus on one in particular; deep neuro-evolution. We'll build an image classifier using a deep neuro-evolutionary algorithm. Enjoy!

Code for this video: https://github.com/harvitronix/neural-network-genetic-algorithm

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More learning resources: http://nn.cs.utexas.edu/?neuroevolution-tutorial-ijcnn2013 https://blog.coast.ai/lets-evolve-a-neural-network-with-a-genetic-algorithm-code-included-8809bece164 https://towardsdatascience.com/paper-repro-deep-neuroevolution-756871e00a66 https://www.youtube.com/watch?v=lu5ul7z4icQ

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A blockchain is a data structure that was first introduced by Satoshi Nakamoto in the Bitcoin protocol white paper almost a decade ago. Bitcoin's blockchain stores transaction data, but we can store any type of data in a blockchain. Ethereum, for example, enables users to store code snippets called 'smart contracts' in their blockchain. In this video, we'll build a simple blockchain in Python that uses proof-of-work consensus, just like the Bitcoin protocol does. There's a lot of misunderstanding around what the blockchain is and what it can do so I hope this demonstrates how simple it really is. Enjoy!

Code for this video: https://github.com/llSourcell/Simple_Blockchain_in_5_Minutes

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How does this apply to AI? Watch this: https://www.youtube.com/watch?v=5Tr13l0O1Ws

More learning resources: https://anders.com/blockchain/blockchain.html https://www.youtube.com/watch?v=wVVGv2bmxow&vl=en https://www.youtube.com/watch?v=LZEHOlZY2To https://www.youtube.com/watch?v=gSQXq2_j-mw https://github.com/llSourcell/Learn_Blockchain_in_2_months

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Facebook is the worlds largest social network with over 2 billion monthly users at the time of this video. It's flagship product, the 'news feed', uses an AI algorithm to personalize the content that each user sees. This AI optimizes for a users attention, as thats what drives its ad revenue based business model. Its an incredibly powerful AI that knows users in ways no human could, synthesizing hundreds of thousands of data points from their browsing activity and web history. In the past few years, we've seen that this AI has had both positive and negative effects on society. I'm going to highlight the negative effects and present 3 possible solutions to fix the problem.

Code for this video: https://github.com/llSourcell/Stream-Framework

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More learning resources: https://www.amazon.com/dp/B079DTVVG8/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1 https://www.youtube.com/watch?v=VyQVlBQCX_Y https://www.youtube.com/watch?v=gSQXq2_j-mw https://bitlab.cas.msu.edu/papers/news_feed_fyi_chi17_lbw.pdf https://medium.com/recombee-blog/recommender-systems-explained-d98e8221f468 https://hbr.org/2018/09/a-blueprint-for-a-better-digital-society http://humanetech.com/problem#the-way-forward https://peepeth.com https://mastodon.social

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Ten Arguments For Deleting Your Social Media Accounts Right Now

Intel released a toolkit that allows developers to build applications that emulate human vision across multiple platforms. The OpenVINO toolkit is based on convolutional neural networks (CNN) and it extends workloads across Intel hardware (including accelerators) and maximizes performance. In this video, I'll explain how OpenVINO, or Open Visual Inference and Neural Network Optimization, enables us to use convolutional neural networks for computer vision on the edge. This video was sponsored by Intel. Enjoy!

Code for this video: https://software.intel.com/en-us/openvino-toolkit/documentation/code-samples

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More learning resources: https://software.intel.com/en-us/openvino-toolkit https://venturebeat.com/2018/05/16/intel-launches-openvino-computer-vision-toolkit-for-edge-computing/ https://www.youtube.com/watch?v=M6Nyh2JDLQs https://software.intel.com/en-us/openvino-toolkit/documentation/get-started https://software.intel.com/en-us/openvino-toolkit/deep-learning-cv

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ComputerVision #SirajRaval #Intel

We can use deep reinforcement learning to optimize a SQL database, and in this video we'll optimize the ordering of a series of SQL queries such that it involves the minimum possible memory/computation footprint. Deep RL involves using a neural network to approximate reinforcement learning functions, like the Q (quality) function. After we frame our database as a Markov Decision Process, I'll use Python to build a Deep Q Network to optimize SQL queries. Enjoy!

Code for this video: https://github.com/llSourcell/SQL_Database_Optimization

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More learning resources: https://rise.cs.berkeley.edu/blog/sql-query-optimization-meets-deep-reinforcement-learning/ https://mldb.ai/ https://docs.microsoft.com/en-us/sql/advanced-analytics/what-is-sql-server-machine-learning?view=sql-server-2017 https://towardsdatascience.com/machine-learning-in-your-database-the-case-for-and-against-bigquery-ml-4f2309282fda https://www.quora.com/Which-database-is-best-for-machine-learning Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

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I'm going to use a quantum processor to help accelerate and extend a machine learning algorithm! Thanks to D-Wave's Leap software, we can access a QPU in the browser. In this live stream, I'll explain how the Leap toolkit works, what a quantum processor offers machine learning engineers, and we'll go through a few relevant examples. Get hype!

Code for this video: https://github.com/llSourcell/quantum_machine_learning_LIVE

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More learning resources: https://cloud.dwavesys.com/leap/resources/learning-docs/ https://hackernoon.com/a-brief-introduction-to-quantum-computing-d21e578cb7ed https://medium.com/xanaduai/quantum-machine-learning-1-0-76a525c8cf69 https://www.nature.com/articles/nature23474

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This is a question I get asked a lot, so I've decided to make a video detailing how to get an AI internship. Internships are a great way to start a career in AI! They enable you to build a professional network and can be amazing learning experiences. I'll list a ton of resources and discuss the most helpful steps in the process including creating a study plan, finding a relevant position, building a personal brand, leveraging your existing network, and practicing for interviews. Enjoy!

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The School of AI: https://www.theschool.ai

I wasn't kidding about the million dollar salaries bit: https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html

Who to Follow in AI: https://medium.com/@alexrachnog/ultimate-following-list-to-keep-updated-in-artificial-intelligence-32776ffcd079

Learn Machine Learning in 3 Months: https://www.youtube.com/watch?v=Cr6VqTRO1v0

How to Learn Math Fast: https://www.youtube.com/watch?v=YzfdL58virc&vl=en

Job listings: https://intern.supply/ https://www.angel.co

Project Ideas: https://github.com/NirantK/awesome-project-ideas https://github.com/llSourcell

How to Read Research Papers: https://www.youtube.com/watch?v=SHTOI0KtZnU&t=42s

How to Write Research Papers: https://www.youtube.com/watch?v=S47RIVkr978

How to Create a Great AI Resume: https://www.youtube.com/watch?v=nMK94JlKRb4

How to Succeed in any Programming Interview: https://www.youtube.com/watch?v=5KB5KAak6tM&t=102s

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SirajRaval

Robotics is a vast field of study, encompassing theories across multiple scientific disciplines. In this video, we'll program a robotic arm in a simulated environment to pick up an object. Along the way, we'll learn about both forward and inverse kinematics. We'll optimize our arms trajectory using calculus and observe how its angles change over time, measuring them with trigonometry. We'll code this in Python, this is an example of machine learning applied to robotic manipulation. Enjoy!

Code for this video: https://github.com/llSourcell/Robotic_Manipulation

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More educational links here: https://blog.robotiq.com/how-to-calculate-a-robots-forward-kinematics-in-5-easy-steps http://courses.csail.mit.edu/6.141/spring2011/pub/lectures/Lec14-Manipulation-II.pdf https://www.alanzucconi.com/2017/04/06/forward-kinematics/ https://appliedgo.net/roboticarm/ http://www.ent.mrt.ac.lk/~rohan/teaching/ME5144/LectureNotes/Lec%205%20Kinematics.pdf

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SirajRaval #Robotic

I'm excited to announce that School of AI is now accepting applications for our research division! We'll select 10 Fall 2018 Fellows and give them 1000 USD in Google Cloud credits each, a personal advisor, and help them submit their work to relevant academic outlets like NIPS and popular journals. Also, 2 reinforcement learning engineers (Laura Graesser and Keng Wah Loon) and I have recently published a static and dynamic version of our white paper titled "SLM Lab". This is a framework for RL research, and we hope that the dynamic version of the paper serves as an example to School of AI researchers. We want our research to be clear, communicative, and applied. I explain the details in this video. Enjoy!

School of AI Grant Application form: https://goo.gl/QRwPdU

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SLM Lab Static Paper: https://goo.gl/xpCZMJ

SLM Lab Dynamic Paper: https://goo.gl/quzUnr

SLM Lab Repository: https://github.com/kengz/SLM-Lab

School of AI Research: https://www.theschool.ai/school-of-ai-fellowship/

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Yes, you can apply as a team.

How to Read a Research Paper: https://www.youtube.com/watch?v=SHTOI0KtZnU&vl=en

How to Write a Research Paper: https://www.youtube.com/watch?v=S47RIVkr978

How to Learn Math Fast: https://www.youtube.com/watch?v=YzfdL58virc&vl=en

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Special thanks to our head of partnerships (Jess Stahl) for helping me define our guidelines.

Google Cloud #AIResearch #SirajRaval

I ask 67 questions to Dr Alan Baratz, the Chief Product Officer of D-Wave Systems. D-Wave has built an incredible quantum computer, and invited me to Vancouver to attend a special launch event of their new Leap system, which allows any developer to use quantum computing very easily in the cloud. In this interview, Alan walks me through the D-Wave facility in Vancouver, and we even get to step inside the quantum computer room. Enjoy!

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Educational Links here: https://www.dwavesys.com/home https://www.youtube.com/watch?v=JhHMJCUmq28 https://hackernoon.com/quantum-computing-explained-a114999299ca https://www.youtube.com/watch?v=LhtnECml-KI&vl=en https://www.youtube.com/watch?v=DmzWsvb-Un4 https://www.clerro.com/guide/580/quantum-computing-explained

Here's his LinkedIn: https://www.linkedin.com/in/alan-baratz-799b786/

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School of AI https://www.theschool.ai

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How am I so productive? I get that question a lot, so in this video I'm going to reveal 5 techniques i use to increase my productivity. For context, I make 3-4 educational videos every week and run an 800 person international organization called School of AI. There's so much to do all the time! Something always needs to be fixed, theres always a new idea to explore, its the adventure of a lifetime. I hope you find these tips useful, this video gets somewhat personal. Enjoy!

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The School of AI: https://www.theschool.ai

This video is dedicated to all Wizards at our School of AI. Thank you for your interest in this incredible technology. I know that together, we'll create a more beautiful world.

More learning resources: https://www.youtube.com/watch?v=toqlhzFJ9fg https://www.youtube.com/watch?v=ijCi4vNYiOc https://www.youtube.com/watch?v=TCERYy48LOk https://www.youtube.com/watch?v=ZXyY_foOel0 https://www.ft.com/content/a3ea16f6-7edd-11e8-bc55-50daf11b720d

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The human brain is wondrous in its capabilities. The rules that govern its prowess at so many tasks are becoming slightly clearer everyday. In this video, I'll highlight how 4 key reinforcement learning algorithms help explain how the human brain works, specifically through the lens of the neurotransmitter known as 'dopamine'. These algorithms have been used to help train everything from autopilot systems for airplanes, to video game bots. TD-Learning, Rescorla-Wagner, Kalman Filters, and Bayesian Learning, all in one go!

Code for this video: https://github.com/llSourcell/Mathematics_of_Dopamine

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The School of AI: https://www.theschool.ai

This video is apart of move 37.

Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: https://www.youtube.com/watch?v=pYyUSh1veoo https://www.youtube.com/watch?v=LyCpuLikLyQ https://www.youtube.com/watch?v=CaCcOwJPytQ https://www.youtube.com/watch?v=C2OUfJW5UNM https://dash.harvard.edu/bitstream/handle/1/23845336/4633133.pdf?sequence=1&isAllowed=y https://web.stanford.edu/group/pdplab/pdphandbook/handbookch10.html

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Google just made it really easy for anyone to use a "Tensor Processing Unit" (TPU) to train machine learning models. Their open-source research tool (Google Colab) now lets developer select a "TPU" as their run-time environment. Are TPUs the next big thing in machine learning? In this video, I'll benchmark the TPU vs the GPU, talk about what the hardware looks like, describe its use cases, then do some TPU specific live programming to train a model to do some natural language processing. Get hype!

Code is here: https://github.com/llSourcell/TPU_Machine_Learning

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The School of AI: https://www.theschool.ai

Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: https://cloud.google.com/blog/products/gcp/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu https://medium.com/@CPLu/should-we-all-embrace-systolic-array-df3830f193dc https://medium.com/intuitionmachine/googles-ai-processor-is-inspired-by-the-heart-d0f01b72defe https://github.com/UCSBarchlab/OpenTPU

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We can use reinforcement learning to build an automated trading bot in a few lines of Python code! In this video, i'll demonstrate how a popular reinforcement learning technique called "Q learning" allows an agent to approximate prices for stocks in a portfolio. The literature of reinforcement learning is incredibly rich. There are so many concepts, like TD-Learning and Actor-Critic for example, that have real-world potential. I hope this video gives you insight into how this incredibly powerful yet simple algorithm works, enjoy!

Code for this video: https://github.com/llSourcell/Q-Learning-for-Trading

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Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: http://www.wildml.com/2018/02/introduction-to-learning-to-trade-with-reinforcement-learning/ http://cs229.stanford.edu/proj2009/LvDuZhai.pdf https://medium.com/@gaurav1086/machine-learning-for-algorithmic-trading-f79201c8bac6 https://github.com/edwardhdlu/q-trader http://www1.mate.polimi.it/~forma/Didattica/ProgettiPacs/BrambillaNecchi15-16/PACS_Report_Pierpaolo_Necchi.pdf

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QLearningForTrading #SirajRaval

The Internet of things lives! More and more devices are coming prepackaged with internet access that wouldn’t normally be. That includes everything from smart salt shakers to smart tabletops. And because these devices have a connection to the world wide web, they can communicate with the outside world and each other, sharing information and even learning from one another. In this video, I'll explain how to use a reinforcement learning technique called "Monte Carlo" to optimize electricity consumption and cooling demands for a smart home. Enjoy!

Code for this video: https://github.com/llSourcell/Internet_of_Things_Optimization

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The School of AI: https://www.theschool.ai

Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: https://www.kth.se/social/files/58b941d5f276542843812288/RL04-Monte-Carlo.pdf https://courses.cs.washington.edu/courses/csep573/12au/lectures/18-rl.pdf https://stat.ethz.ch/education/semesters/ss2016/seminar/files/slides/RL_MCM_heinzer_profumo.pdf https://medium.com/deep-math-machine-learning-ai/ch-12-1-model-free-reinforcement-learning-algorithms-monte-carlo-sarsa-q-learning-65267cb8d1b4 http://www-edlab.cs.umass.edu/cs689/lectures/RL%20Lecture%205.pdf https://github.com/dennybritz/reinforcement-learning/tree/master/MC

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InternetOfThings #IOT #SirajRaval

Two Sigma Investments published a $100,000 code competition on Kaggle that asks data scientists around the world to try their best to create an algorithm that can make predictions about anonymous financial instruments (like derivatives, assets, bonds). Normally, reinforcement learning is not used on Kaggle but in this live stream I'll use reinforcement learning to help solve this challenge. This will serve as a great real-world use case for RL and I'll also discuss some other common time series forecasting methods. Get hype!

Code for this video: https://github.com/llSourcell/Kaggle_Challenge_LIVE-Two-Sigma

Dataset: https://www.kaggle.com/c/two-sigma-financial-modeling/downloads/train.h5.zip

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine...

More Learning Resources: https://www.kaggle.com/kanncaa1/machi... https://www.kaggle.com/rtatman/beginn... https://machinelearningmastery.com/ge... http://blog.kaggle.com/2017/01/23/a-k...

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SirajRaval #KaggleChallenge

Apple just announced its new iPhone XS so expect the demand for it to be massive! We can use a special reinforcement learning algorithm called policy iteration to help Apple manage it's retail inventory and make sure that the demand meets supply, I’ll explain how in this video. We'll assume the role of an AI savvy retail manager for Apple in San Francisco and discuss policy iteration as a solution to our problem. Dynamic programming and real world use cases, enjoy!

Code for this video: https://github.com/llSourcell/iphone_xs_supply_chain

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The School of AI: https://www.theschool.ai

Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa https://artint.info/html/ArtInt_228.html http://kvfrans.com/planning-policy-evaluation-policy-iteration-value-iteration/ https://www.quora.com/How-is-policy-iteration-different-from-value-iteration http://www.inf.ed.ac.uk/teaching/courses/rl/slides15/rl08.pdf

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Sports betting is a popular past-time for many and a great use-case for an important concept known as dynamic programming that I’ll introduce in this video. We'll go over concepts like value iteration, the markov decision process, and the bellman optimality principle, all to help create a system that will help US optimally bet on the winning hockey team in order to maximize profits. Code, animations, theory, and yours truly. Enjoy!

Code for this video: https://github.com/llSourcell/sports_betting_with_reinforcement_learning

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The School of AI: https://www.theschool.ai/courses/move-37-course/

Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: https://artint.info/html/ArtInt_227.html https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa https://www.quora.com/What-is-an-intuitive-explanation-of-value-iteration-in-reinforcement-learning-RL https://www.quora.com/How-is-policy-iteration-different-from-value-iteration https://stackoverflow.com/questions/8337417/markov-decision-process-value-iteration-how-does-it-work

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SirajRaval #SportsBetting #ReinforcementLearning

Google recently released a research framework for fast prototyping of reinforcement learning algorithms called "Dopamine". They say that it aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). In this live stream i'll test it out, try out a few basic reinforcement learning algorithms and compare it to similar frameworks. You can code along with me using CoLab. Get hype!

Code for this video: https://github.com/llSourcell/Google_Dopamine_LIVE

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The School of AI: https://www.theschool.ai

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Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: https://github.com/google/dopamine https://ai.googleblog.com/2018/08/introducing-new-framework-for-flexible.html https://joshgreaves.com/reinforcement-learning/understanding-rl-the-bellman-equations/ https://www.youtube.com/watch?v=N0Ld2iTMaMs

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What is the best way to route data in a network of routers spread out across the globe? This 'internet of things'-based problem can be solved using reinforcement learning! In this video, i'll explain the 2 types of policies, the bellman equation, and the value function. All of these concepts are crucial in the RL pipeline and using animations + code, i'll break them down. Enjoy!

Code for this video: https://github.com/llSourcell/Sensor_Networks

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Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

Take the full course at the School of AI: https://www.theschool.ai

More learning resources: https://becominghuman.ai/the-very-basics-of-reinforcement-learning-154f28a79071 https://medium.freecodecamp.org/an-introduction-to-reinforcement-learning-4339519de419 https://www.oreilly.com/ideas/reinforcement-learning-explained http://kvfrans.com/reinforcement-learning-basics/ https://medium.com/syncedreview/basics-of-computational-reinforcement-learning-fca09f3609ea https://www.toptal.com/machine-learning/deep-dive-into-reinforcement-learning http://www.wildml.com/2016/10/learning-reinforcement-learning/

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SensorNetworks #SirajRaval

Welcome to my new reinforcement learning course titled Move 37! in this 10 week course we'll go over the basics of reinforcement learning up to modern day techniques that involve neural networks called 'deep' reinforcement learning. In this first video, i'll introduce the idea of a Markov Decision Process. This is the basic mathematical framework for framing the reinforcement learning problem. We'll also briefly mention the ideas of a 'policy' and the agent-environment loop. Get hype!

Code for this video: https://github.com/llSourcell/Introduction_Move37

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The School of AI website is undergoing a site migration right now to handle all the traffic + upgrade the layout. It will be down for the next few hours but check back soon. I promise, you won't miss anything and i'll keep you updated via all my social channels: https://www.theschool.ai

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Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

More learning resources: https://towardsdatascience.com/reinforcement-learning-demystified-markov-decision-processes-part-1-bf00dda41690 https://www.cs.rice.edu/~vardi/dag01/givan1.pdf http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture20FinalPart1.pdf https://artint.info/html/ArtInt_224.html

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Let's attempt a Kaggle Challenge together! This time, we'll try to solve the $100,000 "TGS Salt Identification Challenge" using a combination of Google Colab, Conditional Random Fields, and neural networks! Expect some colorful exploratory data analysis, then model building and some Q&A. Get hype!

Code for this video: https://github.com/llSourcell/Kaggle_Challenge_LIVE/

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More learning resources: https://medium.com/@keremturgutlu/semantic-segmentation-u-net-part-1-d8d6f6005066 https://towardsdatascience.com/medical-image-segmentation-part-1-unet-convolutional-networks-with-interactive-code-70f0f17f46c6 https://spark-in.me/post/unet-adventures-part-one-getting-acquainted-with-unet https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/

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Challenge: https://www.kaggle.com/c/tgs-salt-identification-challenge

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SirajRaval #KaggleChallenge #GoogleColab

Everybody Dance Now is a new paper out of UC Berkeley thats able to create photorealistic video of someone dancing in the style of another, more professional dancer. Its like autotune for dance! They trained their model on a source subject (a trained dancer), then were able to transfer that dancing ability onto a target subject. In the video that was generated, the target subject takes on the source subjects dance moves as if it was their own! Incredible work, and it has huge implications for society as a whole. In this video, i'll explain the generative model they used using code and animations, as well as applications of this technology. Enjoy!

Code for this video: https://github.com/llSourcell/Everybody_Dance_Now

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More learning resources: https://www.reddit.com/r/MachineLearning/comments/99qkrk/ruc_berkeley_everybody_dance_now/ https://www.digitaltrends.com/cool-tech/uc-berkeley-deepfake-ai-dance/ https://www.youtube.com/watch?v=PCBTZh41Ris https://www.youtube.com/watch?v=cEBgi6QYDhQ

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DancingAI #SirajRaval #UCBerkeley

The ability to write implementations of machine learning algorithms in pure C allows developers to very efficiently manage memory allocation, concurrency, and control flow. That means fast implementations that can outperform preexisting models in other languages, including even (gasp) Python. It’s a useful skill to know and in this live stream I’ll use C and C-based Python tools like Cython + spaCy to develop some really fast natural language processing algorithms for text data. We’ll be able to tokenize, tag, normalize, vectorize, and dependency parse articles of text to derive valuable insights. No installation necessary, we'll do this together using Google Colab in the browser. Join me, there’s a lot to cover here!

Code for this video: https://github.com/llSourcell/c_programming_for_machine_learning

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More learning resources: https://pydata.org/berlin2016/schedule/presentation/51/ https://smerity.com/articles/2018/cython_for_high_and_low.html https://explosion.ai/blog/writing-c-in-cython https://spacy.io/api/cython https://medium.com/huggingface/100-times-faster-natural-language-processing-in-python-ee32033bdced

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Join me as I teach this, free 10 week reinforcement learning course I’ve called Move 37. I’ll take you on a journey through the basics up to modern day techniques. Every week, we’ll build apps together that will cover both toy and industry problems. You’ll be able to measure your progress along the way by chatting with your peers both online and offline at School of AI chapters globally, taking quizzes, coding challenges, and 2 graded projects. I’ll have weekly coding live streams to help answer any questions, and my assistant instructors will be available to help in our community slack channel. All Wizards who complete the course get an official School of AI Certificate, signed by me. This is going to be wild ride, Signup now and join the movement!

Course Signup Page (Starts September 10th): https://www.theschool.ai/courses/move-37

Github Syllabus: https://github.com/llSourcell/Move_37_Syllabus

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Yes all of my videos will continue to be released on Youtube, every single one. The course website is just so that i can offer non-video content like quizzes, assignments, etc.

My Playlists: https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists

Related Videos: https://www.youtube.com/watch?v=i_McNBDP9Qs&list=PL2-dafEMk2A5FZ-MnPMpp3PBtZcINKwLA

Visit School of AI: https://www.theschool.ai/

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Quantum computers are mind bogglingly powerful machines that take a novel approach to processing data. Built on the principles of quantum mechanics, they utilize complex and fascinating laws of nature that are always there, but usually remain hidden from view like superposition and entanglement. In this video, i'll talk about the intersection of quantum computing and machine learning. Specifically, we'll discuss the examples of quantum annealing, sampling, and quantum gates as layers in a neural network. We'll first try to cover quantum mechanics though, get hype!

Code for this video: https://github.com/llSourcell/quantum_machine_learning

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More learning resources: https://github.com/krishnakumarsekar/awesome-quantum-machine-learning https://hackernoon.com/how-quantum-computing-machine-learning-work-together-bc61d0f1b3a https://www.kdnuggets.com/2018/01/quantum-machine-learning-overview.html https://medium.com/xanaduai/quantum-machine-learning-1-0-76a525c8cf69 https://www.rolandberger.com/en/Point-of-View/The-next-big-thing-Quantum-machine-learning.html https://www.rigetti.com/products

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SirajRaval #Quantum #MachineLearning

Teaching AI is the best way to learn AI. In this video, I'll cover 3 very important topics - how to design an AI curriculum, teaching IRL best practices, and how to create an educational AI youtube video. Tools that we'll use include GitHub, final cut pro, the charisma on command youtube channel, stackoverflow, reddit, and a few others. I hope you find this video useful! I created this video to both help current School of AI Deans create better learning paths for their communities, and to help all Wizards in their journey to master this field. Enjoy!

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Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval

This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

My playlists: https://www.youtube.com/results?search_query=siraj+raval+playlists

Example AI curriculum: https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months

Kaggle Kernels (Great Intro to AI for Beginner Workshops): https://www.kaggle.com/kernels

Another amazing intro to AI tool for workshops: https://github.com/tensorflow/lucid#notebooks

Coursera CNN course: https://www.coursera.org/learn/convolutional-neural-networks

Stanford CNN Course: http://cs231n.stanford.edu/

Carykh's channel: https://www.youtube.com/user/carykh/videos

Charisma on Command: https://www.youtube.com/user/charismaoncommand

Free Learning Management Systems: https://blog.capterra.com/top-8-freeopen-source-lmss/

Learn after effects for animations free: https://www.youtube.com/playlist?list=PLUMFUmbeXFQZ0a-qkeEgx1btFZzAFFfAv

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Learn more about the School of AI: https://www.theschool.ai

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Move 37 is the name of my next course. It will be free, open-source, and will cover reinforcement learning from the basics to modern-day techniques. The pioneers of AI were ambitious dreamers who were laser focused on reverse engineering the complexities of human intelligence to harness it as a tool to benefit humanity. Reinforcement learning is behind the latest advances in AI, and will be a crucial piece to the puzzle of intelligence. I'll update you all more soon, right now I just wanted to start the hype train. Enjoy!

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Over 800 Deans now represent School of AI Chapters across 400 cities globally! We are an international, nonprofit school dedicated to studying, teaching, and creating Artificial Intelligence to help solve the world’s most difficult problems. Deans are guardians of our mission - “To offer a world-class AI education to anyone on Earth for free." and our core values. In this video, I'll describe the work they've done so far, then show you how you can find your nearest School of AI Chapter. Enjoy!

Attend your nearest School of AI Chapter using this map: https://bit.ly/2wldw7s

Is your city not listed? See our more comprehensive, raw list: https://docs.google.com/spreadsheets/d/1bYlde5sxgYTPr-tLADVLF-n4TW5WD8Wl4grubSEFjTk/edit?usp=sharing

Still can't find one? Ask one of our Deans in our Slack community: http://wizards.herokuapp.com/

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Here is yet another list of School of AI Chapters: https://www.google.com/maps/d/u/0/viewer?mid=1fmlKwZZXGoNvZnbZlnPazvTcPcJ89nno≪=27.620243622926218%2C40.21712610000009&z=2

Sign up for my upcoming course, details TBD: https://www.theschool.ai/

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SirajRaval #SchoolOfAI

Let me show you the techniques I use to study machine learning in this video. That includes living a healthy lifestyles, optimizing your learning environment, creating a personalized learning path, prioritizing effectively, and being an active learner. I'll demo the FAST technique, which you can use to help learn faster and more efficiently. I made this with machine learning technology in mind, but these techniques can be used for any field. Enjoy!

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Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology

This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: http://jimkwik.com/kwik-brain-001/ https://github.com/llSourcell/Learn_Deep_Learning_in_6_Weeks https://hbr.org/2018/07/take-control-of-your-learning-at-work https://www.youtube.com/watch?v=nxWfZP6eslM https://www.youtube.com/watch?v=YzfdL58virc https://www.youtube.com/watch?v=cuQMBj1cWPo&t=7s

Here is a v1 list of School of AI Chapters (cleaner list coming ASAP): https://www.google.com/maps/d/u/0/viewer?mid=1fmlKwZZXGoNvZnbZlnPazvTcPcJ89nno≪=27.620243622926218%2C40.21712610000009&z=2

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SirajRaval #HowTo #MachineLearning

Micosoft Azure has a drag and drop interface that lets you build, train, and test models pretty easily. In this video, I'll explain how Azure compares to other cloud offerings, the idea of the hybrid cloud, what its services are, and then we'll use its Machine Learning service to build an automobile price prediction model (linear regression). No one paid me to make this. Enjoy!

Code for this video: https://github.com/llSourcell/azure_machine_learning

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://azure.microsoft.com/en-us/ https://www.youtube.com/watch?v=7pmn6luCwQ4 https://www.youtube.com/watch?v=KXkBZCe699A https://www.youtube.com/watch?v=TjwRj1LrFSo https://www.youtube.com/watch?v=csFDLUYnq4w&t=362s https://www.expeditedssl.com/azure-in-plain-english

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In this interview, I ask Fast.AI researcher Rachel Thomas 67 questions about machine learning and her day to day life. She was selected by Forbes as one of “20 Incredible Women in AI”, was an early engineer at Uber, and earned her math PhD at Duke. She is co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 100,000 students have taken. Rachel is a popular writer and keynote speaker. Her writing has been read by over half a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 8x.

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More Learning resources: http://www.fast.ai/ https://twitter.com/math_rachel https://twitter.com/fastdotai https://www.youtube.com/watch?v=NlqT_MTH-nw

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How can blockchain technology help improve the supply chain? All physical products must take a journey from the factory to the consumer and this journey is called the supply chain. Unfortunately, the path to the consumer isn't straightforward, there are sometimes dozens of intermediaries involved in this process. That includes quality assurance, drivers, procurement officers, etc. Blockchain acts as an immutable store of data, and removes the need for one or more third parties. It can help save both businesses and consumers time and money in this case. In this video I'll demo a solidity app that tracks asset, talk about a real world example called SyncFab, and discuss the different ways blockchain can affect the supply chain. Enjoy!

Code for this video: https://github.com/syncfab/smartcontract

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More Learning Resources: https://syncfab.com/ https://github.com/fivedogit/solidity-baby-steps https://www.youtube.com/watch?v=wVVGv2bmxow&vl=en https://twitter.com/syncfab https://internetofbusiness.com/iot-101-how-blockchain-impacts-manufacturing-and-supply-chains/

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DeepMind's AlphaGo Zero algorithm beat the best Go player in the world by training entirely by self-play. It played against itself repeatedly, getting better over time with no human gameplay input. AlphaGo Zero was a remarkable moment in AI history, a moment that will always be remembered. Move 37 in particular is worthy of many philosophical debates. You'll see what I mean and get a technical overview of its neural components (code + animations) in this video. Enjoy!

Code for this video: https://github.com/Zeta36/chess-alpha-zero

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There are 2 errors in this video: 1. At the top of the residual network, it says value layer twice. One should say 'policy' layer. 2 The residual network is 40 layers, i say 20.

This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://deepmind.com/blog/alphago-zero-learning-scratch/ https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0 https://hackernoon.com/the-3-tricks-that-made-alphago-zero-work-f3d47b6686ef https://web.stanford.edu/~surag/posts/alphazero.html http://tim.hibal.org/blog/alpha-zero-how-and-why-it-works/ http://www.jessicayung.com/alphago-zero-an-overview-of-the-algorithm/

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AlphaGoZero #Deepmind #SirajRaval

How did OpenAI's team of 5 neural networks manage to beat some of the world's best DOTA 2 players? Also, why would OpenAI dedicate so much time and energy to defeating video game players? In this video, I'll explain in detail the cutting edge research techniques OpenAI used to create such an incredible AI algorithm, and how it could be used in the real world. These techniques include Long Short Term Memory Recurrent Neural Networks, Proximal Policy Optimization, and a custom rollout system they've dubbed 'Rapid'.

Code for this video: https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://blog.openai.com/openai-five/ https://towardsdatascience.com/the-science-behind-openai-five-that-just-produced-one-of-the-greatest-breakthrough-in-the-history-b045bcdc2b69 https://blog.openai.com/openai-baselines-ppo/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://www.youtube.com/watch?v=i_McNBDP9Qs&vl=en

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SirajRaval #OpenAI #Dota2

Deepmind released a paper just a few days ago describing a module for neural networks called the Neural Arithmetic Logic Unit (NALU). Although deep neural networks can learn to represent and manipulate numerical information, they don't generalize well outside of the range of numbers encountered during training. Meaning train it on the numbers 1-10 and it won't be able to count to 11. To improve this ability, the researchers created an architecture that represents numerical quantities as linear activations which are manipulated using primitive arithmetic operators, controlled by learned gates. Its really fascinating stuff, i'll detail how it works in this video.

Code for this video: https://github.com/llSourcell/Neural_Arithmetic_Logic_Units

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://arxiv.org/abs/1808.00508 https://github.com/search?l=Python&q=nalu&type=Repositories https://deepmind.com/blog/ https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

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PyTorch is a popular deep learning library released by Facebook's AI Research lab. In this video, I'll explain some of its unique features, then use it to solve the Kaggle "Invasive Species Monitoring Challenge". Sometimes, certain species of plants can slowly destroy an ecosystem if left unchecked. We're going to build a ResNet classifier using PyTorch to help detect which plants need to be removed to help the larger ecosystem survive. Get hype! Starts at 2:18

Code for this video: https://github.com/llSourcell/Pytorch_Coding_Challenge_LIVE

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine...

More Learning Resources: https://github.com/pytorch/examples https://pytorch.org/ https://medium.com/@14prakash/almost-any-image-classification-problem-using-pytorch-i-am-in-love-with-pytorch-26c7aa979ec4 https://www.youtube.com/watch?v=FloMHMOU5Bs

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Binary logistic regression is a machine learning algorithm most useful when we want to model the event probability for a categorical response variable with two outcomes (yes/no, true/false, etc.). In this video we'll build a sentiment classifier app that uses binary logistic regression to classify tweets as either happy, sad, or neutral. I'll use animations, code, rap, skits, and equations to explain how it all works. Enjoy!

Code for this video: https://github.com/llSourcell/logistic_regression

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine...

More Learning Resources: https://github.com/awesomedata/awesome-public-datasets http://www.statisticssolutions.com/what-is-logistic-regression/ https://codesachin.wordpress.com/2015/08/16/logistic-regression-for-dummies/ https://www.youtube.com/watch?v=zAULhNrnuL4 https://machinelearningmastery.com/logistic-regression-for-machine-learning/ https://towardsdatascience.com/the-logistic-regression-algorithm-75fe48e21cfa

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Original repo: https://github.com/guillermo-carrasco/logistic-sentiment

What is the best programming language to learn for machine learning? There are a lot options, and a lot of opinions on this. I'm going to describe the top 3, using code, animations, and data to validate my point. We'll learn about tensorflow.js, several python libraries, and why C++ compiles code so fast. All of that in just 8 minutes, enjoy!

Code for this video: https://github.com/llSourcell/Best-Programming-Languages-for-Machine-Learning

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine...

More Learning Resources: https://www.youtube.com/watch?v=T5pRlIbr6gg&vl=en https://learnpythonthehardway.org/ https://github.com/tensorflow/tfjs-examples https://blog.bitsrc.io/11-javascript-machine-learning-libraries-to-use-in-your-app-c49772cca46c https://www.youtube.com/watch?v=1cHx1baKqq0 http://www.cplusplus.com/doc/tutorial/

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TrustToken is a platform to create asset-backed tokens that you can easily buy and sell around the world. For example, gold to gold tokens or dollar to dollar tokens. We need a stablecoin, and Trusttoken's TrueUSD acts as that. Its pegged to the US dollar, so it provides all of the benefits of cryptocurrency without the volatility. As we move forward into Web 3.0, we need a way to bridge the traditional and crypto financial worlds together. TrustToken does just that, and its why I agreed to work with them (Paid) on this video. I'll go over the architecture, theory, and build a simple solidity smart contract to show that its very doable to build something like this yourself. Enjoy!

Code for this video: https://github.com/trusttoken/trueUSD

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More Learning Resources: https://www.trusttoken.com/ https://blockonomi.com/trusttoken-guide/ https://github.com/llSourcell/TrustToken_Explained

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In this live stream, i'll build a real-time translation app from scratch using Node.JS and Tensorflow.js. We'll learn how machine learning can be used to help translate languages theoretically and programmatically. We'll also learn about how Node + Tensorflow work together and what the modern web development workflow that includes machine learning looks like. Get hype!

Code for this video: https://github.com/llSourcell/Node_JS_Machine_Learning

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine...

More Learning Resources: https://towardsdatascience.com/sequence-to-sequence-tutorial-4fde3ee798d8 https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/ https://dzone.com/articles/quick-introduction-how-nodejs https://github.com/tensorflow/tfjs-examples

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We are School of AI! Our mission is to offer a world-class AI education to anyone on Earth for free. Our doors are open to all those who wish to learn. We are a learning community that spans almost every country dedicated to teaching our students how to make a positive impact in the world using AI technology, whether that's through employment or entrepreneurship. In this video, I make a request for applications to signup as a School of AI Dean for your local city. Deans host learning meetups and are help guide students along their learning journey.

Signup here: https://docs.google.com/forms/d/e/1FAIpQLSdwbOGrPAvmFtohYO1QWxojk_77rEsc1oJSSMGZPtLCG1b8-Q/viewform?usp=pp_url

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We have our first School of AI merchandise as well: https://teespring.com/school-of-ai

This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://www.youtube.com/watch?v=vOppzHpvTiQ https://www.youtube.com/watch?v=T5pRlIbr6gg https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

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Serverless computing is a cloud-computing model where the cloud provider acts as the server, dynamically managing the allocation of machine resources. What that means is 'pay-as-you-use' pricing for the developer for their computing tasks. In this video i'll walk through some useful parts of the Google Cloud suite of services and use it to help train+test a wide+deep neural network to predict customer purchasing power. Topics covered include cloud computing models, kubernetes, Colab, and regression models. Enjoy!

Code for this video: https://github.com/llSourcell/serverless_computing_with_google_Cloud

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://medium.com/google-cloud/gcp-the-google-cloud-platform-compute-stack-explained-c4ebdccd299b https://cloud.google.com/products/ai/ https://cloud.google.com/ml-engine/docs/ https://www.youtube.com/watch?v=gVz9jKE_9iU https://www.youtube.com/watch?v=COSXg5HKaO4 https://www.youtube.com/watch?v=0fsU_2wtzfM

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Amazon Web Service (AWS) offers developers a lot of services, from compute to data storage to serverless functions. In this video, we'll use AWS to train an AI to predict whether or not a customer will churn from using our service. Along the way, I'll explain how different components of the compute service like EC2, Elastic Beanstalk, LightSail, and the EC2 container service work. We'll also look at how SageMaker makes the whole pipeline much faster for beginners. The XGBoost technique will give us some favorable results, and I'll explain why at the end. Amazon did not pay me to make this video. Enjoy!

Code for this video: https://github.com/llSourcell/Amazon_Training_Video

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://www.youtube.com/watch?v=mZ5H8sn_2ZI https://www.youtube.com/watch?v=ubCNZRNjhyo https://www.youtube.com/watch?v=N89AffsxS-g&t=2044s https://aws.amazon.com/blogs/aws/sagemaker/ https://aws.amazon.com/sagemaker

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Join me as I attempt a Kaggle challenge live! In this stream, i'm going to be attempting the NYC Taxi Duration prediction challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. Get hype!

Code for this video: https://github.com/llSourcell/kaggle_challenge

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners https://www.kaggle.com/rtatman/beginner-s-tutorial-python https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/ http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/

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Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems.

Code for this video: https://github.com/llSourcell/loss_functions_explained

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: http://www.informit.com/articles/article.aspx?p=2447200&seqNum=2 https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html https://blog.algorithmia.com/introduction-to-loss-functions/ http://yeephycho.github.io/2017/09/16/Loss-Functions-In-Deep-Learning/ https://stackoverflow.com/questions/42877989/what-is-a-loss-function-in-simple-words http://rohanvarma.me/Loss-Functions/

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Kiki do you love me? The hip-hop artist Drake recently released a song called "in My Feelings' that has become a viral dance challenge. Everyone from Will Smith, to Ciara, to suburban dentists are showing off their dance moves to this popular song as part of the challenge. But rather than submit myself dancing, I've submitted an AI doing it for me. I'll detail how i did it in this video, concluding with a coding challenge.

Coding challenge: https://github.com/llSourcell/InMyFeelings_Challenge

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Lets build a simple machine learning API together! I'll use the now classic neural style transfer algorithm to create a simple API that takes in an image and returns a stylized version of it. We'll use the FloydHub cloud service to both train and serve our model in the cloud. We can easily turn a deep neural network into a REST API that anyone can use, i'll detail those steps in this live stream and we'll build it using Tensorflow.

Code for this video: https://github.com/llSourcell/Machine-Learning-API-Tutorial

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://docs.floydhub.com/getstarted/quick_start/ https://harishnarayanan.org/writing/artistic-style-transfer/ https://medium.com/artists-and-machine-intelligence/neural-artistic-style-transfer-a-comprehensive-look-f54d8649c199 https://blog.paperspace.com/art-with-neural-networks/ https://rare-technologies.com/machine-learning-benchmarks-hardware-providers-gpu-part-2/

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Can we actually predict the price of Google stock based on a dataset of price history? I’ll answer that question by building a Python demo that uses an underutilized technique in financial market prediction, reinforcement learning. The specific technique we'll use in this video is a subset of RL called Q learning. Using a combination of code, animations, and theory i'll explain how we can let our AI learn a policy for when to buy and sell google stock to maximize profit.

Code for this video: https://github.com/llSourcell/Reinforcement_Learning_for_Stock_Prediction

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: http://cs229.stanford.edu/proj2006/Molina-StockTradingWithRecurrentReinforcementLearning.pdf http://www.wildml.com/2018/02/introduction-to-learning-to-trade-with-reinforcement-learning/ https://medium.com/@ranko.mosic/predicting-price-movement-and-trading-using-reinforcement-learning-kearns-nevmyvaka-2013-b5a64daa34f0 https://hub.packtpub.com/develop-stock-price-predictive-model-using-reinforcement-learning-tensorflow/ https://iknowfirst.com/deep-reinforcement-learning-part-2-the-game-of-stock-trading https://www.youtube.com/watch?v=v_L9jR8P-54&list=PLQVvvaa0QuDe6ZBtkCNWNUbdaBo2vA4RO

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Cryptocurrency can be a high-risk, high-reward game for those willing to deal with the volatility. Can we use AI to help us make predictions about Bitcoin's future price? In this video, i'll show you how to build a simple Bitcoin trading bot using an LSTM neural network in Keras. Along the way I'll explain why we use LSTM networks through code and animations, as well as a review of the vanishing gradient problem.

Code for this video: https://github.com/llSourcell/Bitcoin_Trading_Bot

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources: https://medium.com/swlh/developing-bitcoin-algorithmic-trading-strategies-bfdde5d5f6e0 https://bitcoin.stackexchange.com/questions/48093/how-to-build-a-bitcoin-trading-bot https://blog.patricktriest.com/analyzing-cryptocurrencies-python/ https://github.com/lefnire/tforce_btc_trader

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Can we use convolutional neural networks for time series analysis? It seems like a strange use case of convolutional networks, since they are generally used for image related tasks. But in recent months, more and more papers have started using convolutional networks for sequence classification. And since stock prices are a sequence, we can use them to make predictions. In this video, i'll use the popular tensorflow.js library to test out a prediction model for Apple stock. I'll also talk about how recurrent networks work as background. This is my first proper live stream in a year. Get hype!

Code for this video: https://github.com/llSourcell/Financial_Forecasting_with_TensorflowJS

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More learning resources: https://www.youtube.com/watch?v=V8DYi2G7nzg https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/ https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877 https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02 https://www.youtube.com/watch?v=JuLCL3wCEAk

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A lot of times, research papers don't have an associated codebase that you can browse and run yourself. In cases like that, you'll have to code up the paper yourself. That is easier said than done, and in this video i'll show you how you should read and dissect a research paper so you can quickly implement it programmatically. The paper we'll be implementing in this video is called Neural Style transfer, that applies artistic filters to an image using 3 loss functions. Its a great starting point, i'll demo it using code, animations, and math. Enjoy!

Code for this video: https://github.com/llSourcell/Research_to_Code

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More learning resources; https://www.youtube.com/watch?v=-mu3TYZ_udM&t=2s https://www.youtube.com/watch?v=SHTOI0KtZnU https://medium.com/artists-and-machine-intelligence/neural-artistic-style-transfer-a-comprehensive-look-f54d8649c199 https://github.com/anishathalye/neural-style

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The most popular optimization strategy in machine learning is called gradient descent. When gradient descent is applied to neural networks, its called back-propagation. In this video, i'll use analogies, animations, equations, and code to give you an in-depth understanding of this technique. Once you feel comfortable with back-propagation, everything else becomes easier. It uses calculus to help us update our machine learning models. Enjoy!

Code for this video: https://github.com/llSourcell/backpropagation_explained

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More learning resources: https://www.youtube.com/watch?v=XdM6ER7zTLk https://www.youtube.com/watch?v=nhqo0u1a6fw https://www.youtube.com/watch?v=jc2IthslyzM https://www.youtube.com/watch?v=IHZwWFHWa-w https://www.youtube.com/watch?v=umAeJ7LMCfU http://neuralnetworksanddeeplearning.com/chap2.html

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Who’s ready to take the 100 days of ML code challenge? That means coding and/or studying machine learning for at least an hour everyday for the next 100 days. Pledge with the #100DaysOfMLCode hashtag on your social media platform of choice. I’ll give the first few winners a shoutout!

Instructions here: https://github.com/llSourcell/100_Days_of_ML_Code

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There are only 3 rules. 1 Make a public pledge using the #100DaysofMLCode hashtag on your favorite social media platform. 2 Make a public log of your work that you update daily, you can do this via GitHub a blog or a vlog. 3. if you see someone make a post using the #100DaysofMLCode hashtag, give it a like share or comment.

Project Idea: https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

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GitHub is the most popular platform for developers across the world to share and collaborate on programming projects together. In this video, i'll explain how the git protocol works using the analogy of a car company (Tesla), how github works, and then show you from command line how you can push your first repository to github on your own computer.

Git commands in this video: https://github.com/llSourcell/How-to-Use-GitHub

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More learning resources: https://guides.github.com/activities/hello-world/ https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners https://try.github.io/ http://kbroman.org/github_tutorial/ https://blog.udacity.com/2015/06/a-beginners-git-github-tutorial.html

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Social Media can play a big role in every developers life. In this video, I’ll show you how you can use different social media platforms to help you learn cutting tech edge technologies, network, and promote your own brand. I'll talk about the role GitHub, Quora, Youtube, Linkedin, Facebook, and other mainstream platforms play in helping you build a career in this field.

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This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey

More learning resources: https://medium.com/machine-learning-in-practice/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524 https://news.ycombinator.com/ https://www.quora.com/ https://medium.com/@lahorekid/a-list-of-the-best-subreddits-for-data-science-machine-learning-and-data-visualization-84d76b83831e

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Welcome to my new course, Machine Learning Journey! If you’re a student, or between jobs, or in a different field, this 10 week course will help you learn everything you need from marketing your skills to building a solid mathematical foundation in order to get a job or start your own venture as a machine learning engineer or data scientist. I'm going to show you how to write a great resume in this first video. There are some key things to keep in mind and it depends on the company you're applying to. I'll cover it all, enjoy!

Curriculum for this course: https://github.com/llSourcell/Machine_Learning_Journey

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More learning resources: lhttps://www.linkedin.com/in/sirajraval/ https://www.theschool.ai/pages/jobs?p=1 https://novoresume.com/ https://www.wordclouds.com/ https://www.topresume.com/

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Ask me anything! In this Q&A i talk about blockchain, AI, VR, learning techniques, and a bunch of other software related topics. Next week i'll have a more quality live streaming setup, so I'll be able to use the green screen for coding. Enjoy!

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More learning resources: https://deepmind.com/blog/neural-scene-representation-and-rendering/ https://deepmind.com/research/publications/neural-scene-representation-and-rendering/ https://www.teslarati.com/google-deepmind-ai-neural-scene-representation/ https://venturebeat.com/2018/06/14/googles-deepmind-develops-ai-that-can-render-3d-objects-from-2d-pictures/

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DeepMind recently released a new paper called "Neural Scene Representation and Rendering". Its pretty amazing stuff, I have to admit. Their AI, if given just a single image of an environment, can generate a 3D map of that environment. That includes all the objects, textures, colors, and shapes that it sees. In this video,. I'll talk about its applications, theory, and code it up myself in Keras even though they haven't released any code to give you a programmatic example.

Code for this video: https://github.com/llSourcell/Generative-Query-Network

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More learning resources: https://deepmind.com/blog/neural-scene-representation-and-rendering/ https://deepmind.com/research/publications/neural-scene-representation-and-rendering/ https://www.teslarati.com/google-deepmind-ai-neural-scene-representation/ https://venturebeat.com/2018/06/14/googles-deepmind-develops-ai-that-can-render-3d-objects-from-2d-pictures/

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There needs to be a bridge between the centralized Web and the decentralized Web. In this video, i'll talk about a protocol called Essentia. It acts as a gateway between both worlds, connecting apps, dApps, assets, devices, storage etc. into one easy interface. Its a great example of this and i'll also discuss multi-signature transactions, cross atomic swaps, and some other blockchain related techniques using code.

Code for this video: https://github.com/llSourcell/Decentralized_Gateways

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More Learning Resources: https://essentia.one/ https://www.youtube.com/watch?v=aPVmd7SyKfQ&t=352s https://medium.com/essentia_one/essentia-one-launches-11m-fund-to-foster-the-growth-of-projects-built-upon-its-protocol-c3f373fab538 https://en.bitcoin.it/wiki/Atomic_cross-chain_trading https://en.bitcoin.it/wiki/Multisignature

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The blockchain is transforming every single industry. The amount of talent and capital that has started moving to this space is remarkable. This is the teaser trailer for my next course dedicated to the blockchains real business use cases. If you are a someone who's serious about learning how this technology works and wants to make a positive impact in the world, this is the course for you.

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More learning resources: https://medium.com/crypto-oracle/enterprise-blockchain-has-arrived-2d2e4d8ec0d https://www.blockchaintechnology-news.com/2018/06/21/microsofts-blockchain-for-copyright-could-become-worlds-biggest-enterprise-network/ https://blockchainatberkeley.blog/a-snapshot-of-blockchain-in-enterprise-d140a511e5fd https://entethalliance.org/

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Whether you're interested in AI or you just want to do some real engineering work, you’re going to need to brush up on your math skills. In this video, I’ll describe my strategy to learn mathematics as fast as possible. Math is a specific, powerful vocabulary for ideas and giving a structure to the way you learn it will empower you to absorb much more of it much faster. I'll go over my strategies in order.

Math resources: https://github.com/llSourcell/learn_math_fast

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More learning resources: http://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf https://brilliant.org/ https://triseum.com/variant-limits/

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What kind of laptop should you get if you want to do machine learning? There are a lot of options out there and in this video i'll describe the components of an ideal laptop for ML. I'll also mention the ideal desktop, DIY machine, and cloud option. We'll discuss how RAM, GPUs, CPUs, motherboards, hard drives, and other components affect training and inference time. This video was not sponsored.

Only a few days left to signup for my dapps course! https://www.theschool.ai

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Helpful resources: https://lambdal.com/raw-configurator?product=quad https://www.nvidia.com/en-us/geforce/products/10series/laptops/ https://www.google.com/chromebook/device/acer-chromebook-11/ https://medium.com/yanda/building-your-own-deep-learning-dream-machine-4f02ccdb0460 https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415

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You can build a sustainable full-time income from doing freelance AI programming work. In this video, i'm going to show you the steps you can take to start your journey as a freelancer. Whether you're a student or are employed full-time, you can begin the process of planning out a freelance career today. Getting clients, leveling up your skills, marketing yourself, setting up your financials, tools to help optimize your workflow, these are all aspects of the freelance life that i'll explain from my own personal experience.

List of resources: https://github.com/llSourcell/AI_Freelancing

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Job marketplaces: http://upwork.com/ https://www.freelancer.com/ https://www.toptal.com/ https://angel.co/ http://founderdating.com/ https://www.guru.com/ https://weworkremotely.com/

Networking: https://www.kdnuggets.com/meetings/ https://conferences.oreilly.com/strata https://www.datasciencecentral.com/

Tools: https://www.codementor.io/freelance-rates https://www.bunq.com/business https://www.hellobonsai.com/

Learning resources: https://github.com/Mybridge/machine-learning-open-source/blob/master/src/05-2018.md https://www.youtube.com/playlist?list=PL2-dafEMk2A6oABirZ1Ug805Ag-8W54rN https://www.youtube.com/playlist?list=PL2-dafEMk2A5_Fcpl3FHOjo2Gfios3b5o

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The Movidius Neural Compute Stick is a miniature deep learning hardware development platform that you can use to prototype, tune, and validate, your AI at the edge. Intel reached out and asked if I would do a video for them, and since I thought the product was cool I said yes. They mailed it to me and I've had a lot of fun using it. In this video, i'll talk about how it works and demo an image classification demo in Python using it.

Code for this video: https://github.com/llSourcell/Deep_Learning_with_Intel

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More learning resources: https://developer.movidius.com https://www.pyimagesearch.com/2018/02/12/getting-started-with-the-intel-movidius-neural-compute-stick/ https://medium.com/deep-learning-turkey/a-brief-guide-to-intel-movidius-neural-compute-stick-with-raspberry-pi-3-f60bf7683d40 https://github.com/movidius/ncsdk

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Neuroevolution is a technique that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, topology and rules. We can think of it as an optimization strategy, similar to gradient descent, that can in some cases be faster to converge and give better results. We're going to simulate evolution by creating neural network creatures in javascript and having them evolve over time to try and walk across the screen. I'll discuss the steps that our algorithm will take, then code out the neural network class at the end of the video.

Code for this video: https://github.com/llSourcell/Modeling_Evolution_with_TensorflowJS

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More learning resources: https://medium.com/tensorflow/a-gentle-introduction-to-tensorflow-js-dba2e5257702 http://thecodingtrain.com/CodingChallenges/100.2-neuroevolution-flappy-bird.html https://becominghuman.ai/designing-ai-solving-snake-with-evolution-f3dd6a9da867 https://www.cs.ucf.edu/~kstanley/neat.html https://blog.coast.ai/lets-evolve-a-neural-network-with-a-genetic-algorithm-code-included-8809bece164 https://eng.uber.com/deep-neuroevolution/

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Google invited me to attend their annual developer conference called Google I/O in Mountain View California. I went ahead and vlogged the experience using a GoPro camera that they sent me. In this video, you'll see my journey to IO including the car ride, meeting developers, some really cool machine learning demos, and the concert afterwards. I am posting this since only because i got a few requests for it. It was a fun experience and I encourage people to go if they get the opportunity.

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More learning resources: https://events.google.com/io/ https://www.techradar.com/news/google-io-2018 https://guce.oath.com/collectConsent?brandType=nonEu&.done=https%3A%2F%2Ftechcrunch.com%2F2018%2F05%2F08%2F8-big-announcements-from-google-i-o-2018%2F%3Fguccounter%3D1&sessionId=3_cc-session_1c1ba8b4-d26e-425b-894a-2f833b0cca29⟨=&inline=false https://www.theverge.com/google-io

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A recent paper called 'world models' has gotten really popular in the machine learning community. They trained an AI to play a racing game by having it learn inside of its own simulated dream environment. Meaning, the AI learned a model of what the game world was like, then was able to generate a game world that was roughly similar to what it learned and train inside of that. A simulation inside of a simulation. I'll explain how their model was structured both theoretically and programmatically in this video.

Code for this video: https://github.com/llSourcell/world_models

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More learning resources: https://worldmodels.github.io/ https://arxiv.org/abs/1803.10122 https://www.reddit.com/r/MachineLearning/comments/87nqbz/r_world_models/ https://medium.com/applied-data-science/how-to-build-your-own-world-model-using-python-and-keras-64fb388ba459 https://news.ycombinator.com/item?id=16860247 https://towardsdatascience.com/world-models-in-tensorflow-episode-1-2b3c217ebc8f

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Pose estimation is a pretty fun machine learning problem to work on and with Tensorflow.js anyone can implement their own pose estimation algorithm that works in the browser with just a few lines of code. In this video i'll go over some of tensorflow.js' interesting features, compare it to other frameworks, then go into the history and algorithmic detail of the pose estimation algorithm. We'll end the video with me programming a pose estimation algorithm in javascript.

Code for this video: https://github.com/llSourcell/pose_estimation

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More learning resources: https://medium.freecodecamp.org/a-quick-introduction-to-tensorflow-js-a046e2c3f1f2 https://towardsdatascience.com/deep-learning-in-your-browser-a-brisk-guide-ca06c2198846 https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5 https://github.com/tensorflow/tfjs-models/tree/master/posenet https://js.tensorflow.org/

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Deep Learning is the dark art of our times. Incredibly powerful, mysteriously accurate, and accessible to just about anyone. In this video, i've compiled an open source 6 week curriculum to help you learn deep learning using various sources from the Web. I'll describe all of my learning resources, why i chose them, and how they can help you. Starting with feedforward networks, to convolutional networks, recurrent networks, adversarial learning, and finally deep reinforcement learning. Enjoy!

Curriculum for this video: https://github.com/llSourcell/Learn_Deep_Learning_in_6_Weeks/

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Machine learning algorithms make predictions based on a dataset. If that dataset is a collection of musical notes, the prediction would be a new collection of musical notes. We can consider that prediction the AI's unique composition. The question is, can an AI really compose music as well as humans can? In this video i'll go over some really popular models that have been used to generate music, from hidden markov models, to recurrent networks (with their variations), to the modern generative adversarial network. Code, theory, and demos included in this video. Enjoy!

Code for this video: https://github.com/llSourcell/AI_For_Music_Composition

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More learning resources: https://medium.com/artists-and-machine-intelligence/neural-nets-for-generating-music-f46dffac21c0 http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/ https://magenta.tensorflow.org/ https://www.ampermusic.com/ https://blogs.technet.microsoft.com/machinelearning/2017/12/06/music-generation-with-azure-machine-learning/ https://salu133445.github.io/musegan/

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Self driving cars are the future of transportation and will make up a crucial part of society as more drive related jobs are automated. In this video, i'll explain how the entire self driving car pipeline works, including computer vision, path planning, control, sensor fusion, and localization. We'll use the Udacity simulator to train our own self driving car with the Keras deep learning library as a tool at the end. This technology is surprisingly simple to understand, it just requires research into a couple of subfields, all of which i'll cover.

Code for this video: https://github.com/llSourcell/self_driving_cars_explained

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More learning resources: https://www.ucsusa.org/clean-vehicles/how-self-driving-cars-work#.WwGlx9MvwmI https://medium.com/swlh/everything-about-self-driving-cars-explained-for-non-engineers-f73997dcb60c https://hackernoon.com/self-driving-cars-explained-db9fc8ced7e8 https://searchenterpriseai.techtarget.com/definition/driverless-car https://www.youtube.com/watch?v=xMH8dk9b3yA https://www.youtube.com/channel/UCq0imsn84ShAe9PBOFnoIrg https://www.youtube.com/watch?v=FTr3n7uBIuE&t=1782s

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Every product in your home is there as a result of being distributed across whats called a supply chain. The path that a commodity takes through manufacturing, transport, distribution centers, etc. is called the supply chain. The supply chain for most companies is riddled with inefficiencies. Late drivers, bad weather conditions, suboptimal planning, timing issues, the list of things that can go wrong is endless and most of this coordination is done by humans. AI can be used to optimize this entire pipeline, from planning to autonomous transport. In this video i'll demo an app using IBM's Logistic Wizard to optimize a simulated companies supply chain, as well as build a time series forecasting model using Keras to predict the price of a shipment on a certain date.

Code for this video: https://github.com/llSourcell/AI_Supply_Chain

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More learning resources: https://www.tandfonline.com/doi/abs/10.1080/13675560902736537 https://news.crunchbase.com/news/alloy-raises-12-million-to-bring-ai-to-supply-chain-management/ https://www.americanexpress.com/us/content/foreign-exchange/articles/using-AI-in-supply-chain-management/ https://www.forbes.com/sites/oracle/2018/03/07/data-the-new-ai-supply-chain/#2499f7b16049 https://www.ibm.com/us-en/marketplace/supply-chain-insights?S_PKG=OV60982&cm_mmc=Search_Google-_-IBM+Watson+Customer+Engagement_Watson+Supply+Chain+-+Supply+Chain+Insights-_-WW_NA-_-ai+in+supply+chain_Exact_OV60982&cm_mmca1=000020LZ&cm_mmca2=10006656&cm_mmca7=9061263&cm_mmca8=aud-295225167539:kwd-427960545262&cm_mmca9=9f6dfb3c-4b94-4f53-a67e-219704bef887&cm_mmca10=265871013815&cm_mmca11=e&mkwid=9f6dfb3c-4b94-4f53-a67e-219704bef887|1467|21744&cvosrc=ppc.google.ai%20in%20supply%20chain&cvo_campaign=000020LZ&cvo_crid=265871013815&Matchtype=e https://medium.com/@KodiakRating/6-applications-of-artificial-intelligence-for-your-supply-chain-b82e1e7400c8

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Recruiting is a 200 billion dollar industry thats all about judging potential job candidates and seeing if they're a good fit for a position at a company. Recruiters receive thousands of resumes and are responsible for analyzing all of them. Theres essentially a massive amount of data that these humans have to parse through and find the best ones. This is easily a problem machine learning can solve, we'll build an app that can classify resumes into 27 different job categories using natural language processing via a convolutional neural network. I'll explain how in this video. Also its midterm time, so see the link below for the midterm assignment.

Code for this video (with midterm): https://github.com/llSourcell/AI_for_Resumes

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More learning resources: https://medium.com/the-mission/how-i-turned-my-resume-into-a-bot-and-how-you-can-too-f03847352baa https://www.textkernel.com/challenges-behind-parsing-matching-cvs-jobs/ https://www.quora.com/How-do-I-develop-a-resume-parser-using-NLP-Natural-Language-Processing?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa https://dzone.com/articles/cv-r-cvs-retrieval-system-based-on-job-description https://www.slideshare.net/zainulsayed39/218-intelligent

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AI allows for apps that can handle the complexity of scheduling a meeting between one person or a group of people. This is useful for recruiters and all sorts of teams. Natural Language processing, a subset of AI, focuses on learning from linguistic constructs and deriving meaning from it in a structured way. We'll build a slack bot that uses NLP to read the intent of the user and schedule a meeting accordingly. I talk about the architecture, code, and need for this software in this video. I also give some tips on how I use github near the end.

Code for this video: https://github.com/llSourcell/AI_for_Scheduling

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More learning resources: https://x.ai/ https://www.stottlerhenke.com/products/aurora/ https://www.myally.ai/ https://www.youtube.com/watch?v=XzkgjtP9lFQ https://blog.init.ai/tutorial-building-a-conversational-booking-bot-with-init-ai-and-acuity-scheduling-e717df35adf6

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Can we build a chatbot that acts as a conversational agent for a company brand? Yes! In this video we'll go over different techniques that let you build your own chatbot using AI technology. Specifically, we'll be discussing generative models and deep neural networks. There are a bunch of services we can use to build a chatbot using no code, but thats no fun right? I'll talk about a few of them as well as some startups that are doing amazing things in this marketing space using chatbots as a tool to engage their customers over messaging apps instead of social media. Tensorflow, python, and motivation are all we need!

Code for this video: https://github.com/llSourcell/chatbot_tutorial

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More learning resources: https://aws.amazon.com/lex/?sc_channel=PS≻_campaign=lex_2017≻_publisher=google≻_medium=awns_lex_nb≻_content=chatbot_p≻_detail=chatbot≻_category=lex≻_segment=209069302261≻_matchtype=p≻_country=US&s_kwcid=AL!4422!3!209069302261!p!!g!!chatbot&ef_id=Wt@YdwAABBtmpBYI:20180506204248:s https://apps.worldwritable.com/tutorials/chatbot/ https://chatbotsmagazine.com/tutorials/home https://chatbotsmagazine.com/how-to-develop-a-chatbot-from-scratch-62bed1adab8c https://chatbottutorial.com/ https://www.ibm.com/watson/how-to-build-a-chatbot/

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Audience targeting and automatic content creation are just a few of the many ways AI can be used to help grow your user base and increase sales. In this video, i'll go over some startups that are applying AI to the marketing space and then programmatically walk through some AI techniques like matrix factorization, SVD, and LSTM neural networks that help a marketer outperform the competition and get the optimal results for their business. We've got quite a lot to cover in this video!

Code for this video: https://github.com/llSourcell/AI_In_Marketing

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More learning resources: https://www.youtube.com/watch?v=cdLUzrjnlr4 https://www.youtube.com/watch?v=BwmddtPFWtA https://www.thinkwithgoogle.com/marketing-resources/ai-personalized-marketing/ https://medium.com/the-mission/how-to-boost-your-marketing-with-artificial-intelligence-8c092d7e3f7d https://www.youtube.com/watch?v=9gBC9R-msAk

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Can AI be used for fraud prevention? Yes! In this video, we'll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We'll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we'll try it out on a transaction dataset.

Code for this video: https://github.com/llSourcell/AI_for_Financial_Data

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More learning resources: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877 https://www.youtube.com/watch?v=GlV_QO5B2eU https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://pythonprogramming.net/python-programming-finance-machine-learning-framework/ https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676 https://www.quantopian.com/posts/simple-machine-learning-example

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Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow.

Code for this video: https://github.com/llSourcell/AI_in_Finance

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More learning resources: https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://www.datacamp.com/community/tutorials/finance-python-trading http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/ https://www.udacity.com/course/machine-learning-for-trading--ud501 https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code

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Tensorflow.js is Google's new Javascript verison of its popular Machine Learning library Tensorflow. This allows developers, hobbyists, and researchers to build & train AI models in the browser! It allows for both training and inference to happen entirely client-side, which means we can utilize our users GPUs (all types). This is really exciting, it opens up a whole new world of machine learning possibilities, especially for training on real-time data streams directly from our users. I'll explain the code behind an object detection app using the library in this video, as well as the different parts of the library.

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More learning resources: https://www.youtube.com/watch?v=Y_XM3Bu-4yc&t=561s https://www.youtube.com/watch?v=YB-kfeNIPCE https://github.com/tensorflow/tfjs-examples https://js.tensorflow.org/

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Can AI be used to detect various diseases from a simple body scan? Yes! Normally, doctors train for years to do this and the error rate is still relatively high. From mammograms to cat scans, AI can diagnose a disease better than any human can if given the right training dataset. This will drastically reduce patient death, save medical practices a lot of money, and aid doctors in the patient care process. Everyone will win and its important to remember that AI won't replace doctors, it will become the most powerful tool they've ever used. And once enough AI startups start impacting the field of healthcare, it will become as common a tool as the stethoscope has been.

Code for this video: https://github.com/llSourcell/AI_in_Medicine_Clinical_Imaging_Classification

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Curriculum: https://github.com/llSourcell/AI_For_Business_Curriculum

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How do we use AI to cure drug discovery? This is apart of my AI for business series right here on Youtube. Subscribe to stay up to date! In this video I'm going to cover how the drug discovery process works in clinical labs and how AI can be used to speed up that process by orders of magnitude. We'll look at 3 different papers that used different types of neural networks, and the last one is what we'll focus on; the General Adversarial Network.

Code for this video: https://github.com/llSourcell/AI_for_healthcare

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Curriculum: https://github.com/llSourcell/AI_For_Business_Curriculum

More learning resources: https://github.com/plotly/dash-drug-discovery-demo https://www.youtube.com/watch?v=FTr3n7uBIuE&t=25s https://www.youtube.com/watch?v=yz6dNf7X7SA https://www.youtube.com/watch?v=Sw9r8CL98N0 http://www.healthcareitnews.com/slideshow/how-ai-transforming-healthcare-and-solving-problems-2017

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Tensorflow is an popular open source machine learning library released by Google that allows developers to build ML models easily. How are you supposed to build an AI business using Tensorflow? There are so many startups and companies using Tensorflow in production environments with sometimes millions of users relying on the software. In this video i'm going to explain how all the different tools in the Tensorflow ecosystem (Serving, Lite, Imperative Programming, Visualizations, etc.) work together to allow the developer to use an end to end pipeline for developing and deploying AI software. We'll go over code, theory, and we'll think through two busines use cases for applying AI to a problem. Hope you like it!

Code for this video: https://github.com/llSourcell/Make_Money_with_Tensorflow

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More learning resources: https://www.slideshare.net/ETS_Asset_Management_Factory/python-tensorflow-how-to-earn-money-in-the-stock-exchange-with-deep-learning-pycones2017-talk https://stratechery.com/2015/tensorflow-and-monetizing-intellectual-property/ https://towardsdatascience.com/6-ways-people-are-making-money-with-machine-learning-db10575f3564 https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow https://www.technologyreview.com/s/544356/heres-what-developers-are-doing-with-googles-ai-brain/ https://www.computerworlduk.com/open-source/what-is-tensorflow-how-are-businesses-using-it-3658374/ https://pseudoprofound.wordpress.com/2016/08/19/why-tensorflow-is-free-and-what-you-can-do-about-it/

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What is this HashGraph and will it really make blockchain obsolete? In this video I talk about the consensus algorithm that it uses, what the data structure looks like, and what the application specific use cases are. I want to stress that HashGraph is a private ledger, so of course its going to be more efficient in terms of transactions per second than Bitcoin or Ethereum. Its like comparing apples and oranges. There is a public version of HashGraph in the works, but in terms of bringing that kind of scalability to a public ledger, we'll just have to wait and see if its possible. This talk includes code, theory, and of course yours truly. Enjoy!

Code for this video: https://github.com/llSourcell/HashGraph_Explained

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Crypto-kitties! I'm going to cover the most important parts of the popular crypto-kitties game built on the Ethereum blockchain in this video. The game is over 2000 lines of Solidity code and it allows players to buy, sell, and breed these collectible cats. At one point, this game accounted for a third of transactions on the Ethereum network which is absolutely insane. This is a great example of a wildly popular use case for a decentralized application. There is a lot of potential here to make a game that people obsess over and makes both the creators and players a good amount of money. Enjoy!

Code for this video: https://github.com/llSourcell/Cryptokitties

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Stephen Merity is a senior research scientist working on deep learning in San Francisco with Salesforce Research via the MetaMind acquisition. He's been lucky enough to work with fascinating people and groups over the years including Google Sydney, Freelancer.com, the Schwa Lab at the University of Sydney, the team at Grok Learning, the non-profit Common Crawl, and IACS @ Harvard. I met him at Nvidia's GTC conference in San Jose and asked him 67 Questions about his life and thoughts on Machine Learning. Enjoy!

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More Learning resources: https://www.linkedin.com/in/smerity/ https://twitter.com/Smerity https://smerity.com/abme.html

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Are you too busy to dedicate 4 years of your life to a traditional Computer Science Major? I've created a 5 month accelerated Computer Science curriculum to help you get a broad overview of the field, covering the most important topics in sequential order using the free resources of the Internet. I've listed learning tips, Computer Scientists to follow, and a path in this video. I hope you find it useful, this is the kind of learning path I'd design for myself but I'm open sourcing it. Enjoy!

Curriculum for this video: https://github.com/llSourcell/Learn_Computer_Science_in_5_Months

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People to follow on Twitter: Jeff Dean Paul Allen Tim Berners-Lee Linus Torvalds Brendan Eich John Carmack

Curriculum: Week 1-2 (Learn Python) - https://automatetheboringstuff.com/ - https://www.codecademy.com/learn/learn-python

Week 3-4 (Data Structures) - https://www.edx.org/course/data-structures-fundamentals-uc-san-diegox-algs201x

Week 5-6 (Algorithms) - https://courses.csail.mit.edu/6.006/fall11/notes.shtml

Week 7 (Databases) - https://www.coursera.org/learn/python-databases

Week 8 (Networking) - https://www.coursera.org/learn/computer-networking

Week 9-10 (Web Development) - https://www.youtube.com/watch?v=1u2qu-EmIRc&list=PLhQjrBD2T382hIW-IsOVuXP1uMzEvmcE5 - https://github.com/melanierichards/just-build-websites

Week 11-12 (Mobile Development) - https://developer.apple.com/library/content/referencelibrary/GettingStarted/DevelopiOSAppsSwift/ - https://developer.android.com/training/basics/firstapp/index.html

Week 13-14 (Data Science) - https://www.edx.org/course/python-for-data-science

Week 15-16 (Computer Vision) - https://www.coursera.org/learn/python-text-mining

Week 17-18 (Natural Language Processing) - https://www.udacity.com/course/introduction-to-computer-vision--ud810

Week 19 (Software Engineering Practices) - https://www.coursera.org/learn/software-processes

Week 20 (Blockchain) - https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL

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In this video i'm going to cover a decentralized data exchange called DataWallet. It' allows users to earn a passive income from their data by getting rewarded either via a network token (DXT) or via personalized services. Users complete ownership over their own data, while giving developers and data consumers access to a marketplace for high-quality data.

Code for this video: https://github.com/llSourcell/datawallet

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More learning resources: https://datawallet.com/ https://datawallet.com/pdf/datawallet_whitepaper.pdf https://medium.com/@DataWalletHQ http://solidity.readthedocs.io/en/v0.4.21/introduction-to-smart-contracts.html https://blockgeeks.com/guides/solidity/

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I'm proud to have collaborated with DataWallet to make this video. They're a great team!

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Dr Pieter Abbeel got his PhD at Stanford University under the mentorship of Andrew Ng and went on to become a professor at UC Berkeley. He's worked at OpenAI, Willow Garage and now Embodied Intelligence.Drawing on recent advances in Deep Imitation Learning and Deep Reinforcement Learning, Embodied Intelligence is developing AI software that makes it easy to teach robots new, complex skills. I caught him after he gave a lecture at Nvidia's GTC Conference in San Jose and asked him some really quick questions about his life and his thought on Machine Learning. Enjoy!

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More Learning resources: https://people.eecs.berkeley.edu/~pabbeel/ https://twitter.com/pabbeel?lang=en https://scholar.google.com/citations?user=vtwH6GkAAAAJ&hl=en

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Solve it or I'll die trying! I shot this music video in the desolate outskirts of London. The warehouse was cold AF, we had GPUs training models, Satoshi Nakamoto shows up to help me drop sick beats, it was dope. This isn't a game, we're trying to save the world here. This is my manifesto. It's our underground movement that will spark the new Web and the rise of true Artificial Intelligence. This is the School of AI. Enjoy!

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More learning resources: https://www.youtube.com/watch?v=QfNvhPx5Px8 https://github.com/bitcoinbook/bitcoinbook/blob/develop/book.asciidoc

Lyrics:

When they update the weights, They call it back-propagate, Thats what Every single section of these new papers say, GPU, go, turn it up, accel-le-rate, Tryna minimize that loss till its A-OK, Word2Vec Input in Dot product Activate Do it once Do it twice Input out Errors Done I don’t need a label I’ll just learn do it without one As long as I’m breathing i’ll predict the outcome

Solve it or I’ll die tryin Solve It or I’ll die tryin Solve it or I’ll die tryin Solve it or I’ll die tryin

My oh my, who’s this Satoshi? Shout out to the movement best known as BTC, I was rocking blockchains back in twenty fourteen Before Cuban thought that chips mint currency Tx in Tx Out Make a block Mine it up Fork the chain, Commit once Commit twice Start a strain Lets start a religion that gets cod-di-di-ified And this is my life, I call it the School of AI, Go!

Solve it or I’ll die tryin Solve It or I’ll die tryin Solve it or I’ll die tryin Solve it or I’ll die tryin

So Kimmys got a nuke, oh my effing G From California to Japan, on that F or C Um will anybody stop it? How bout you or me Cause Ima fight it to the end like a true O G

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Peer to peer chat without using a server? We can do this using Ethereum's whisper library. This is a chat protocol that lets nodes chat with each other directly, no need for a central server. It doesn't even use a centralized routing source to let nodes discover each other like BitTorrent does with trackers, instead it uses a distributed hash table as a tool for decentralized peer discovery. Yes its not completely real-time, but the trade-off allows for true private chat. I'm going to explain how it works and code the demo at the end.

Code for this video: https://github.com/llSourcell/Decentralized_Chat

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More learning resources: https://ethereum.stackexchange.com/questions/127/what-is-whisper-and-what-is-it-used-for https://hackernoon.com/our-progress-on-ethpay-encrypted-chatting-via-whisper-6a9550ef036a https://github.com/ethereum/go-ethereum/wiki/How-to-Whisper https://github.com/ethereum/wiki/wiki/Whisper

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Blockchain technology is hot right now! I've devised a 2 month study plan to help you learn how blockchains and cryptocurrency works. This is the kind of curriculum I'd create for myself to learn, but I'm open sourcing it for you guys since I love you. We'll start with cryptography, move on to Bitcoin, then Ethereum, other cryptos, and finally Decentralized Applications.

Curriculum for this video: https://github.com/llSourcell/Learn_Blockchain_in_2_months

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Week 1 (Cryptography): https://www.coursera.org/learn/crypto

Week 2 (Mastering Bitcoin + Annotated BTC Whitepaper + BTC wallet) https://github.com/bitcoinbook/bitcoinbook http://fermatslibrary.com/s/bitcoin https://bitcoin.org/en/choose-your-wallet

Week 3 (Bitcoin and Cryptocurrencies + build a blockchain) https://www.coursera.org/learn/cryptocurrency https://hackernoon.com/learn-blockchains-by-building-one-117428612f46

Week 4 (Ethereum + Annotated Eth Whitepaper): https://www.udemy.com/blockchain-application/ http://fermatslibrary.com/s/ethereum-a-next-generation-smart-contract-and-decentralized-application-platform#email-newsletter

Week 5 (Solidity Programming + Ethereum articles): https://cryptozombies.io/ https://blockgeeks.com/?s=ethereum

Week 6 (Other Cryptocurrencies): https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL

Week 7 (Decentralized Applications): http://shop.oreilly.com/product/0636920039334.do

Week 8 (Build a Decentralized Application): https://github.com/moshest/p2p-index

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Everyone fails. In this video, i'll recount 5 times in my life where I failed and talk about how I recovered. Whether it be in work life, school life, or personal life, failure is just a reality of life. Its how you deal with it that defines your future. If you're wondering, I recorded this is Lisbon Portugal since I was invited to speak at a Data Science meetup about blockchain AI. I took this is my airbnb when i had some free time.

Hammad's Winning Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Binary%20Logistic%20Regression/Binary%20Logistic%20Regression.ipynb

Wladi's Runner up code: https://github.com/wladiarce/logistic_regression_numpy

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More learning resources: https://simpleprogrammer.com/overcoming-obstacles-stoic-mindset/ https://blog.todoist.com/2015/04/14/overcome-fear-of-failure/ https://www.quora.com/How-can-I-overcome-the-fear-of-failure-especially-fear-of-coding https://thenextweb.com/dd/2015/06/11/8-barriers-to-overcome-when-learning-to-code/

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Welcome to Web 3.0! I'm going to cover what Web 3.0 is, how a blockchain works (visually), what new kinds of apps are now possible, and at the end we'll write our first smart contract. This video is apart of the Decentralized Applications course found at www.theschool.ai

Code for this video: https://github.com/llSourcell/Web3.0_Explained

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Sign up for the next course at The School of AI: http://theschool.ai/

More learning resources: https://blockgeeks.com/guides/what-is-cryptocurrency/ https://blog.ethereum.org/author/vitalik-buterin/ https://medium.com/@VitalikButerin https://codeburst.io/build-your-first-ethereum-smart-contract-with-solidity-tutorial-94171d6b1c4b

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Do you need to know math to do machine learning? Yes! The big 4 math disciplines that make up machine learning are linear algebra, probability theory, calculus, and statistics. I'm going to cover how each are used by going through a linear regression problem that predicts the price of an apartment in NYC based on its price per square foot. Then we'll switch over to a logistic regression model to change it up a bit. This will be a hands-on way to see how each of these disciplines are used in the field.

Code for this video (with coding challenge): https://github.com/llSourcell/math_of_machine_learning

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Sign up for the next course at The School of AI: http://theschool.ai/

More learning resources: https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568 https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/ https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning https://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf

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How would a global basic income system work? Is that even possible? Using recent advances in blockchain technology we can create a system that gives people a recurring income just for existing. This idea has been around for decades, but only now that we have the power to create global trustless systems can we detach the idea from the context of nation-states and into the context of distributed systems. I'll explain how 2 basic income coins, grant coin and circles work in this video.

Code for this video: https://github.com/llSourcell/basic_income

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Sign up for the next course at The School of AI: http://theschool.ai/

More learning resources: http://www.grantcoin.org/ https://joincircles.net/ http://reddit.com/r/cryptoubi

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2 days left to enroll in my new course, learn more and signup here - https://www.theschool.ai/courses/decentralized-applications

I'm going to list 10 people that inspire me so that hopefully you get some inspiration from them as well. These people are AI researchers, blockchain researchers, artists, and CEOs. This list is in no particular order, I'll list all of their social profiles below so you can follow them. Lets get started!

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1 Naval Ravikant

https://twitter.com/naval Mind-blowing interview: https://www.youtube.com/watch?v=IrSn3zx2GbM&t=468s

2 Oriol Vinyals

https://twitter.com/OriolVinyalsML

3 Andrew Trask

https://twitter.com/iamtrask My interview with Andrew: https://www.youtube.com/watch?v=qJ1rdVEcl5g&t=340s

4 Balaji Srinivasan

https://twitter.com/balajis On Exit: https://www.youtube.com/watch?v=cOubCHLXT6A&t=572s

5 Bryan Johnson

https://twitter.com/bryan_johnson interview: https://www.youtube.com/watch?v=L3t8-8Z5w5U

6 Tristan Harris

https://twitter.com/tristanharris Amazing podcast: https://www.youtube.com/watch?v=jlPF9_1VIso

7 Tupac Shakur

https://www.youtube.com/watch?v=GL-ZoNhUFmc

8 Juan Benet

https://www.youtube.com/watch?v=iUVLuXjPAfg&t=5724s

9 Steve Jobs

My fav video: https://www.youtube.com/watch?v=keCwRdbwNQY&t=275s

10 Trent McConaughy

https://www.youtube.com/watch?v=P1txT3kdJRE

Cryptocurrency and video game assets? There is indeed an intersection, the emerging esports industry is growing rapidly. Cryptocurrency offers a solution to gamers to allow them to spend their in-game currency outside of the game world. In this video, I'll talk about several cryptocurrencies that are fueling the esports movement and in the process break down how the proof of stake consensus algorithm works as well as the token distribution algorithm in Solidity.

Slides for this video: https://github.com/llSourcell/Crypto_game_tokens

Enroll in The School of AI: https://www.theschool.ai/courses/decentralized-applications

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More Learning resources: https://www.xpcoin.io/ https://blockonomi.com/gaming-esports-cryptocurrencies/ https://wax.io/ https://icoinblog.com/top-3-esports-altcoins-cryptocurrency-tokens-2018/

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5 days left to enroll, learn more and signup here - https://www.theschool.ai/courses/decentralized-applications

Baidu has released some really impressive research that enables them to generate a voice in the style of anyone after having been trained on only a few examples. Few-shot generative learning is something i'm particularly interested in, and in this video I'll go over what their progress has looked like in this field over the past 2 years. We'll go over a web demo of audio generation, try and understand how DeepMind's WaveNet (similar) works, and then look at some Tensorflow code to get a deeper understanding of how this model plays out programmatically.

Code for this video: https://github.com/llSourcell/Neural_Network_Voices

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More learning resources: https://github.com/baidu-research/deep-voice https://thenextweb.com/artificial-intelligence/2018/02/26/baidus-ai-can-clone-your-voice-and-give-it-a-different-gender-or-accent/ http://research.baidu.com/deep-voice-3-2000-speaker-neural-text-speech/ http://research.baidu.com/neural-voice-cloning-samples/

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Learn more and join the program at https://www.theschool.ai/courses/decentralized-applications

Wizards I'm so excited to be able to launch this finally! Artificial Intelligence and blockchain technology have enabled a new breed of application software popularly called 'Decentralized Apps'. I wrote the popular O'Reilly book titled 'Decentralized Applications" and now I'm creating a brand new online course to teach you how to build your own Decentralized app using blockchains, distributed hash tables, peer to peer protocols, and deep learning! This is the first course in The School of AI and the students that sign up for it will receive weekly videos, live streams, Q&A office hours, exclusive projects, a certificate of completion, expert feedback and review to teach the foundations of this future-shaping technology.

How is a total beginner supposed to get started learning machine learning? I'm going to describe a 3 month curriculum to help you go from beginner to well-versed in machine learning. Its an accelerated learning plan, something i'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, the machine learning theory, and the deep learning theory to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

Curriculum from this video: https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months

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Month 1

Week 1 Linear Algebra https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ Week 2 Calculus https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr Week 3 https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2 Week 4 Algorithms https://www.coursera.org/courses?languages=en&query=Algorithm%20design%20and%20analysis

Month 2

Week 1 learn python for data science https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Math of Intelligence https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D Intro to Tensorflow https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Week 2 Intro to ML (Udacity) https://eu.udacity.com/course/intro-to-machine-learning--ud120

Week 3-4 ML Project Ideas https://github.com/NirantK/awesome-project-ideas

Month 3 (Deep Learning)

Week 1 Intro to Deep Learning https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

Week 2 Deep Learning by Fast.AI http://course.fast.ai/

Week 3-4 Re-implement DL projects from my github https://github.com/llSourcell?tab=repositories

ML people to follow on Twitter: https://www.quora.com/Who-should-I-follow-on-Twitter-to-get-useful-and-reliable-machine-learning-information

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How does Amazon's MXNet Deep Learning framework compare to the other deep learning frameworks, especially tensorflow? It's got an imperative programming API and it does well with distributed training. I'll go over some key features then start coding different data structures using its symbol, ndarray, and module API.

Code for this video: https://github.com/llSourcell/MXNet

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More learning resources: https://www.youtube.com/watch?v=GBkOMtc9BIk https://www.youtube.com/watch?v=iLqEhCqCQ70 https://www.youtube.com/watch?v=kGktiYF5upk https://www.youtube.com/watch?v=ZYVPdzEsJAQ

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This is a teaser trailer for The School of AI.

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How are you supposed to get in on the AI hype? Deep learning has enabled a whole new breed of applications, and there are still so many different opportunities to apply it in fields that are completely untapped. I'll go through the steps you need to take to start your own AI startup using a combination of my own experiences and best practices from the industry as a guide. From data collection to model training to picking a problem, we'll try to understand this challenging task.

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Sources: https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists https://www.deeplearning.ai/ http://www.fast.ai/ http://www.deeplearningbook.org/ https://www.kaggle.com/datasets https://github.com/awesomedata/awesome-public-datasets https://archive.ics.uci.edu/ml/datasets.html

More learning resources: https://www.youtube.com/watch?v=CBYhVcO4WgI https://www.youtube.com/watch?v=bNpx7gpSqbY https://www.youtube.com/watch?v=JqxzLUE6pP8 https://www.youtube.com/watch?v=ii1jcLg-eIQ https://www.youtube.com/watch?v=ia8arCDoxZ8 https://www.youtube.com/watch?v=677ZtSMr4-4

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Nvidia published a recent paper that detailed a system able to progressively grow the architecture of a generative adversarial network during the training phase. This network was able to produce images nearing photorealism at a faster pace than previous models, pretty exciting stuff! I'll explain how it works both in theory and code in this video.

Code for this video: https://github.com/llSourcell/Progressive_GANs/tree/master

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More learning resources: https://www.youtube.com/watch?v=yz6dNf7X7SA https://www.youtube.com/watch?v=Sw9r8CL98N0 https://www.youtube.com/watch?v=HN9NRhm9waY https://www.youtube.com/watch?v=deyOX6Mt_As https://www.youtube.com/watch?v=5WoItGTWV54

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Can we land a SpaceX Falcon Heavy Rocket in simulation using machine learning? Yes! Reinforcement learning is a technique that lets an agent learn how best to act in an environment using rewards as its signal. OpenAI released a library called Gym that lets us train AI agents really easily. We'll use a combination of the Tensorflow and gym libraries to build an RL agent capable of landing a rocket perfectly. The specific technique we're using is called proximal policy optimization, this is an actor-critic algorithm that is really popular. Lets get started!

Code for this video: https://github.com/llSourcell/Landing-a-SpaceX-Falcon-Heavy-Rocket

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More learning resources: https://www.youtube.com/watch?v=2pWv7GOvuf0 https://www.youtube.com/watch?v=e3Jy2vShroE https://www.youtube.com/watch?v=lvoHnicueoE https://www.youtube.com/watch?v=xWe58WGWmlk https://www.youtube.com/watch?v=PtAIh9KSnjo https://www.youtube.com/watch?v=2xATEwcRpy8

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I'm going to go over the steps you can take to write your first research paper! Research papers have long been something only academics did, but the Internet has offered us several ways to democratize this process. Journals like Arxiv are open for public submissions, machine learning papers are generally open source so anyone can learn from them, and online communities offer advice in the way previously only a professor could. I'll go through these tips in order in as much detail as I can on how to write a research paper.

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More learning resources: https://www.youtube.com/watch?v=1AYxMbYZQ1Y https://www.youtube.com/watch?v=UiTaxAfIBPg https://www.youtube.com/watch?v=oPobmEZ4lfs&t=242s https://www.youtube.com/watch?v=KlgR1q3UQZE https://www.youtube.com/watch?v=DS2DOEkorDo&t=220s

OpenAI's request for research: https://openai.com/requests-for-research/

Some of my papers: http://www.sirajcoin.io/whitepaper.html https://docs.google.com/document/d/1QFyBUV8pKqgl__4J1zT0BmIYfTYF8hnlyalOo7PJvLM/edit?usp=sharing (i turned this one into a book actually [Decentralized Applications])

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Mathematics is its own language, and not enough people speak this language. I'm going to show you some key steps necessary for you to be able to read any math equation. Memorization techniques, grammar, structure, rules, it all comes together to help you form an intuition around the language of the Universe. Machine Learning, cryptography, robotics, all of the cool topics in Computer Science use math heavily so its best to master it when you can. I'll go over 2 my thought process of analyzing 2 equations for some papers to give you some insight into how I think about these things.

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Deep Learning cheatsheet: https://hackernoon.com/deep-learning-cheat-sheet-25421411e460

Math of Computer Science at MIT: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/

Math of Intelligence: https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D

More learning resources: https://www.youtube.com/watch?v=Syj8FVr7vbM https://www.youtube.com/watch?v=ze3bDrg1tJ8 https://www.youtube.com/watch?v=8i9-9zHbW6g https://www.youtube.com/watch?v=l3XzepN03KQ

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Can Ripple be considered a cryptocurrency? Is it centralized? Why is it getting adopted by so many banks so fast? Lots of questions, I'm going to answer them all in this video. We'll also build a simple minimalist Ripple Client to act as a wallet in javascript using the Ripple API. I'll go over the architecture and explain the concepts of gateways, market makers, and validator nodes in the network.

Code for this video: https://github.com/llSourcell/Ripple_Explained

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More learning resources: https://www.youtube.com/watch?v=dsr_yD-EQbM https://www.youtube.com/watch?v=W3aM6HoNbe4 https://www.youtube.com/watch?v=Dcb1GIF7p6s https://www.youtube.com/watch?v=kwyIEMyMfqs https://www.youtube.com/watch?v=qaoCHVya5JA https://ripple.com/build/

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Blockchain technology, Deep Learning, and Interplanetary File Systems? How can we merge all this cool new technology to create something useful and beautiful? I'll go over the underlying technologies that can help create decentralized artificial intelligence. I visited Singapore for a short stint recently and gave this talk at a local data science meetup. The crowd was full of energy and excitement. I went ahead and re-uploaded the video they posted to my own channel, since I think you guys could benefit from it as well. Its a 40 minute talk with 5 minute Q&A at the end.

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More learning resources: https://www.youtube.com/watch?v=b57J2dAt2Y0 https://www.youtube.com/watch?v=GWOdAAFoSFE https://www.youtube.com/watch?v=gSQXq2_j-mw https://www.youtube.com/watch?v=5Tr13l0O1Ws

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Can machine vision map humans from videos to 3D Models? Yes! DensePose is a new architecture by the team at Facebook AI research that does just that. It uses a convolutional network with some special features like region of interest pooling and cascading to make this happen. It was also trained on a newly created labeled dataset that mapped human poses to 3D models. The team open sourced the dataset but not the code, but using the details in the paper we can recreate their results. I'll explain how it works in this video.

Code for this video: https://github.com/llSourcell/3D_Pose_Estimation

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More learning resources: https://arxiv.org/abs/1802.00434 http://densepose.org/ https://www.youtube.com/watch?v=pW6nZXeWlGM&t=157s https://www.youtube.com/watch?v=2CWVgTNremI https://www.youtube.com/watch?v=NnzzSkKKoa8 https://www.youtube.com/watch?v=hyHNpl0xSkw

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How can we build AI that creates AI? This concept actually already exists! Google uses self-creating AI as part of its AutoML service that finds the best model for customers. Genetic algorithms and neuroevolutionary strategies offer us a way to replicate the process of natural selection en silico. I'll talk about ways of using machine learning algorithms to create better machine learning algorithms in this video.

Code for this video (with coding challenge): https://github.com/llSourcell/neuroevolution-for-flappy-birds

Der Waldi's Winning Code: https://github.com/DerWaldi/youtube-video-face-swap

Paramdeep's Runner up Code: https://github.com/ParmuSingh/face_swap

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More learning resources: https://www.youtube.com/watch?v=YNLC0wJSHxI https://www.youtube.com/watch?v=nA3YOFUCn4U https://www.youtube.com/watch?v=B682RpmK0Xk https://www.youtube.com/watch?v=Qh2yT-AL1V8 https://www.youtube.com/watch?v=gn4nRCC9TwQ http://gekkoquant.com/2016/03/13/evolving-neural-networks-through-augmenting-topologies-part-1-of-4/

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How are machine learning and neuroscience related? I'll discuss some of the discoveries in neuroscience that have produced breakthroughs in machine learning from the past, present, and that will in the future in this video. Neural Networks were inspired by the brain and some of the most recent breakthroughs in AI continue to be inspired by discoveries in neuroscience (like episodic memory in the case of Deep Q).

Code for this video: https://github.com/llSourcell/machine_learning_and_neuroscience

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More learning resources: https://www.youtube.com/watch?v=7kiTiiMSCrI https://www.youtube.com/watch?v=_cYIvfS-knA https://www.youtube.com/watch?v=sJfe_b784Go https://www.youtube.com/watch?v=6lHHxcxurhQ https://www.youtube.com/watch?v=8ZXlUyyYJCg

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Theres a new trend on the interwebs called 'Deepfakes', a machine learning system that can be trained to paste one person's face onto another person's body, complete with facial expressions. The effect isn't yet more convincing than conventional computer graphics techniques, but it could democratize Hollywood-level special effects fakery - and, potentially, lead to a flood of convincing hoaxes. I'll explain how DeepFakes works both programmatically and thoeretically in this video. It's essentially 2 autoencoders trained on 2 image datasets and then we reconstruct image A using image B's decoder.

Code for this video (with coding challenge): https://github.com/llSourcell/deepfakes

Challenge winner: https://github.com/ivanklimuk/DNA_to_RNA

Runner-up (Paramdeep): https://github.com/ParmuSingh/autoencoder-mnist

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More learning resources: https://www.youtube.com/watch?v=Uivy6vnP2B0 https://www.youtube.com/watch?v=yhvK_VFVfhw https://www.youtube.com/watch?v=8Pm4EDofw4c https://www.youtube.com/watch?v=nCSOLVw6gi8 https://www.youtube.com/watch?v=RL-wyUEqXYM

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Lets predict the price of Bitcoin using social media data from both Reddit and Twitter with the power of machine learning! We're going to use a technique called sentiment analysis to find the emotions behind the users and try to see if the price correlates with how people are feeling about the popular cryptocurrency. We'll use the CoinDesk API to retrieve bitcoin price data and python to program our app.

Code for this video: https://github.com/llSourcell/bitcoin_prediction

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More learning resources: https://www.youtube.com/watch?v=QLVMqwpOLPk https://www.youtube.com/watch?v=CQ5kc_j4RjA https://www.youtube.com/watch?v=SSu00IRRraY https://www.youtube.com/watch?v=ftMq5ps503w https://www.youtube.com/watch?v=tcCfExxcoaU https://www.youtube.com/watch?v=brtRJxebL58 https://www.youtube.com/watch?v=JuLCL3wCEAk https://www.youtube.com/watch?v=1E3BLED5D6c

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Dynamic programming is used heavily in Artificial Intelligence! Famous problems like the knapsack problem, problems involving the shortest path conundrum and of course the fibonacci sequence can all benefit from the solutions dynamic programming provides. I'm going to go over a short demo then talk about the types of dynamic programming and when to use it.

Code for this video (with jupyter notebook): https://github.com/llSourcell/dynamic_programming

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More learning resources: https://www.youtube.com/watch?v=W2ote4jCuYw https://www.youtube.com/watch?v=RI1Ey1LkpxQ https://www.youtube.com/watch?v=OQ5jsbhAv_M https://www.youtube.com/watch?v=cYT-JTZPpWc https://www.youtube.com/watch?v=vaGRbiTSEkQ

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How does an autoencoder work? Autoencoders are a type of neural network that reconstructs the input data its given. But we don't care about the output, we care about the hidden representation its learned. Its a lower dimensional compression of the input that preserves its features. We can use this learned representation for tasks like image colorization, dialogue generation, and anomaly detection.

Code for this video (with Coding Challenge): https://github.com/llSourcell/autoencoder_explained

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More learning resources: http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/ http://ai.stanford.edu/~quocle/tutorial2.pdf https://lazyprogrammer.me/a-tutorial-on-autoencoders/ https://blog.keras.io/building-autoencoders-in-keras.html https://jaan.io/what-is-variational-autoencoder-vae-tutorial/ https://hackernoon.com/autoencoders-deep-learning-bits-1-11731e200694

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Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience.

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More learning resources: http://www.arxiv-sanity.com/ https://www.reddit.com/r/MachineLearning/ https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/ https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/ http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper

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The math behind cryptography is immensely fascinating, I could spend all day studying it! We're going to go over some fundamental cryptographic concepts like hashing, zero knowledge proofs, and my favorite 'ZK-Snarks'. This is quite an in-depth video, i had to pick and choose the topics i wanted to dive into more. There is so, so much i could talk about. Each of these topics could deserve their own course. Cryptography is going to be paramount to building future decentralized Artificial Intelligence systems that we can both control and protect from attackers.

Code for this video: https://github.com/llSourcell/crypto_math/blob/master/Crypto%20Math.ipynb

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More learning resources: https://www.tutorialspoint.com/cryptography/ https://gpgtools.tenderapp.com/kb/how-to/introduction-to-cryptography https://www.khanacademy.org/computing/computer-science/cryptography https://www.ibm.com/developerworks/tivoli/tutorials/s-crypto/s-crypto.html https://openmined.org/

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Today I'm introducing a cryptocurrency for our community called SirajCoin. It will act as the fuel for our global community of developers to engage with me directly by spending it on my attention via hourly meetings and video collaborations. It will also unlock access to exclusive content, merchandise, and events. Importantly, the coin enables a novel governance structure in our community that gives members voting powers on how to best spend community funds. These funds can be used to fund AI research initiatives, elect community representatives, and empower other creators that spread AI knowledge. This cryptocurrency is the first step in establishing The School of AI, a world-wide network of programming wizards dedicated to solving AI through research and education. This is not about me making money, if i wanted to do that i would've said yes to one of the 1000 offers i get from big tech companies to sell out and make videos about their products on this channel. and no one would ever know.

Code for this video: https://github.com/llSourcell/sirajcoin

3 IMPORTANT NOTES 1. The sirajcoin.io/your_public_key referral link system doesn't work yet, next week it will. I will update the community when it works. 2. If you want some free SirajCoin right now, post your public address in the youtube comments section and I will send you some ASAP (if you have subscribed to this channel). 3. A lot of the features of SirajCoin are a work in progress, and I'll need the help of our community to build them. So make issues and pull requests in the GitHub repo to help with this effort. The better this project goes, the better off we all will be.

Code for this video: https://github.com/llSourcell/sirajcoin

SirajCoin website: www.sirajcoin.io

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More learning resources: https://github.com/keppel/lotion https://github.com/tendermint/tendermint https://tendermint.com/

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Learn how to create your own simple token in about 5 minutes wth the help of this video! I'm gong to show you how to create a cryptocurrency using Ethereum's smart contract platform and the ERC20 token creation protocol. This is currently the most popular way to create tokens and you can get this whole thing up and running in about an hour. I'll go over some relevant tools like MetaMask and the remix compiler as well.

Code for this video: https://github.com/llSourcell/simple_token

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More learning resources: https://www.ethereum.org/token https://steemit.com/ethereum/@maxnachamkin/how-to-create-your-own-ethereum-token-in-an-hour-erc20-verified https://ether.direct/2017/08/22/ethereum-beginners-guide-create-a-simple-cryptocurrency/ https://medium.com/simple-token/simple-token-prepares-flying-start-to-2018-12154670dae9 https://simpletoken.org/ https://hashnode.com/post/how-to-build-your-own-ethereum-based-erc20-token-and-launch-an-ico-in-next-20-minutes-cjbcpwzec01c93awtbij90uzn

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Can we predict cryptocurrency prices using machine learning? We're going to build a Keras deep learning model that attemps to predict the future price of cryptocurrencies like Bitcoin and Ethereum in this video. The type of model i'm using is a bidirectional LSTM recurrent network. Ethereum future prices as well as other cryptocurrency prices are hard to predict, but with the power of machine learning we can find a suitable prediction.

Code for this video: https://github.com/llSourcell/ethereum_future

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More learning resources: https://dashee87.github.io/deep%20learning/python/predicting-cryptocurrency-prices-with-deep-learning/ https://nicholastsmith.wordpress.com/2017/11/13/cryptocurrency-price-prediction-using-deep-learning-in-tensorflow/ https://github.com/PiSimo/BitcoinForecast https://github.com/philipperemy/deep-learning-bitcoin

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Creating 3D Artificial Intelligence in a simulated world is actually pretty easy using Unity 5. It's a powerful tool and I'll go over its new ML Agents toolkit, that allows researchers and developers to build/train ML models in a 3D simulation. It's pretty fun watching it in a 3D world, lets break down the code and concepts. Please Subscribe! That's what keeps me going.

EDIT: I made a mistake, minecraft was not created in Unity, but it has been recreated in Unity countless times.

Code for this video (Please hit the red subscribe button for more): https://github.com/llSourcell/Unity_ML_Agents

Brendan's Winning Code: https://github.com/bhansconnect/alpha_zero_othello

Shadman's Runner Up Code: https://github.com/kudchikarsk/Happines-Prediction

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More learning resources: https://unity3d.college/2017/11/01/unity3d-machine-learning-writing-a-custom-agent-create-your-own-ai-bot/ https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Getting-Started-with-Balance-Ball.md https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/ https://software.intel.com/en-us/articles/explore-unity-technologies-ml-agents-exclusively-on-intel-architecture

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Credits to sykoo for the unity footage

How does Numenta's hierarchical temporal memory system work? I'm going to go over it's technological stack and how it differentiates from deep learning. Numenta is trying to solve Artificial General Intelligence by replicating the human neocortex in silicon.

Code for this video: https://github.com/llSourcell/numenta_explained

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More learning resource: https://numenta.org/htm-school/ https://numenta.com/papers-videos-and-more/ https://numenta.com/blog/2017/11/14/secret-to-strong-ai/ https://numenta.com/blog/2017/11/01/new-experiments-shed-light-on-htm-theory/ https://numenta.org/resources/HTM_CorticalLearningAlgorithms.pdf

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Whats the best way to get started with deep learning? Keras! It's a high level deep learning library that makes it really easy to write deep neural network models of all sorts. It can use several popular backends like Tensorflow and CNTK. I'll show you how it works and explain how it compares to the other deep learning libraries.

Code for this video: https://github.com/llSourcell/keras_explained

Alberto's Winning Code: https://github.com/alberduris/Reinforcement_Learning_AI_Video_Games/tree/master/Week%206

Sven's Runner-up Code: https://github.com/EmbersArc/PPO

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More learning resource: https://elitedatascience.com/keras-tutorial-deep-learning-in-python https://keras.io/ https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ https://github.com/fchollet/keras-resources https://www.datacamp.com/community/tutorials/deep-learning-python https://dashee87.github.io/data%20science/deep%20learning/python/another-keras-tutorial-for-neural-network-beginners/

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Cardano is a 3rd generation blockchain led by the co-founder of Ethereum, Charles Hoskinson and Jeremy Wood (ex-Ethereum). They've synthesized a lot of cool ideas from what they've learned in the space like delegated proof of stake consensus, smart contract functionality, KYC compliance, and a more suitable networking protocol than TCP/IP called RINA. I'll go into the technical details in this video and compare it to other alternatives.

Code for this video: https://github.com/llSourcell/how_does_cardano_work

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More learning resources: https://www.cardanohub.org/en/the-daedalus-wallet/ https://blockonomi.com/cardano-guide/ https://coincodex.com/crypto/cardano/guides/ https://github.com/liuchengxu/blockchain-tutorial/tree/master/cardano https://ru-clip.com/video/teJkEpTax8U/how-to-buy-cardano-ada-cryptocurrency.html https://blog.coinswitch.co/how-to-buy-ada-cardano-from-coinswitch-667d8b37de0 https://www.ruclip.tk/video/4oKOl01ycOs/how-to-buy-cardano-ripple-altcoins-step-by-step-tutorial/

I was just making another video but got an unexpected call from an alien world. In this video, I help a girl named North from another planet help find a missing device using Artificial Intelligence. We use machine learning/deep learning technologies to help find the device in the mountain of data. Using programming, we'll try to search for it. This video is made for the young and young at heart.

Code for this video: https://github.com/llSourcell/Make_a_neural_network/blob/master/demo.py

Coding challenge winners, i'll announce you guys in a separate video this weekend. To everyone, Please Subscribe! And like. And comment. That's what keeps me creating.

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2 notes, i acknowledge that I -used a feedforward net not a convnet. -didn't write out the full details of the activation function

More learning resources: https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/ https://machinelearningmastery.com/basic-concepts-in-machine-learning/

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Monero is a cryptocurrency that enables private transactions. That means the sender, receiver, and transaction amount are not publicly viewable, unlike Bitcoin's blockchain. Monero's transactions stay private using the technology of ring signatures, ringCT, stealth addresses, and I2P routing. I'll explain how all of this works in this video. I do not condone the use of this technology for illegal transactions. This is powerful stuff, ideally, we start using this as a stepping stone towards a world where we get paid for our transactional data (and all the rest of our data).

You'll find the jupyter notebook for this video and the associated code that I demo in the github link below.

Code for this video: https://github.com/llSourcell/how_does_monero_work

More learning resources: https://getmonero.org/ https://github.com/monero-project/monero https://www.monero.how/tutorial-how-to-use-the-monero-gui-wallet https://www.monero.how/tutorial-how-to-mine-monero https://99bitcoins.com/beginners-guide-to-monero/

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What do advancements in AI mean for the military? Military robotics has come a long way with advancements in machine learning, the soaring affordability of computing power, and the rise of cloud computing. I'll talk about how AI is used on the battlefield and how we can prevent a SkyNet scenario from occuring.

Code for this video: https://github.com/llSourcell/proximal_policy_optimization

More learning resources: https://www.robotictechnologyinc.com/images/upload/file/Presentation%20Military%20Memetics%20Tutorial%2013%20Dec%2011.pdf http://faculty.cse.tamu.edu/murphy/IROS2011Tutorial.htm https://insights.sei.cmu.edu/sei_blog/2017/06/army-robotics-in-the-military.html https://www.technologyreview.com/s/603795/the-us-military-wants-its-autonomous-machines-to-explain-themselves/ https://gcn.com/articles/2017/05/22/dod-ai-machine-learning.aspx https://www.engadget.com/2017/05/15/the-pentagon-is-hunting-isis-using-big-data-and-machine-learning/

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Blockchains are so 2017. 2018 will be all about BlockDAGs. I'll explain a cryptocurrency system called IOTA that is getting really popular and how it differs from a traditional blockchain. At the end I'll show you how you can buy some.

Code for this video: https://github.com/llSourcell/IOTA_demo

More learning resources: https://learn.iota.org/tutorials https://learn.iota.org/tutorial/payments-and-messaging-leaderboard https://www.youtube.com/watch?v=MsaPA3U4ung https://iotasupport.com/buyingiotaotc.shtml https://iotasupport.com/gettingstarted.shtml

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I recently spoke at a conference in Helsinki Finland called Slush about decentralized AI (Blockchain + machine learning applications). It was a wild 3 days and the energy in the arena was amazing. I went ahead and uploaded my talk from Slush to share with you guys. Hope you like it!

More learning resources: https://www.coindesk.com/information/what-is-a-decentralized-application-dapp/ https://www.coindesk.com/7-cool-decentralized-apps-built-ethereum/ https://www.safaribooksonline.com/library/view/decentralized-applications/9781491924532/ch01.html

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Reinforcement learning is hot right now! Policy gradients and deep q learning can only get us so far, but what if we used two networks to help train and AI instead of one? Thats the idea behind actor critic algorithms. I'll explain how they work in this video using the 'Doom" shooting game as an example.

Code for this video: https://github.com/llSourcell/actor_critic

i-Nickk's winning code: https://github.com/I-NicKK/Tic-Tac-Toe

Vignesh's runner up code: https://github.com/tj27-vkr/Q-learning-conv-net

Taryn's Twitter: https://twitter.com/tarynsouthern

More learning resources: https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf http://rll.berkeley.edu/deeprlcourse/f17docs/lecture_5_actor_critic_pdf.pdf http://web.mit.edu/jnt/www/Papers/J094-03-kon-actors.pdf http://mlg.eng.cam.ac.uk/rowan/files/rl/06_actorcritic.pdf http://mi.eng.cam.ac.uk/~mg436/LectureSlides/MLSALT7/L5.pdf

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Can we generate music with AI? Taryn Southern, a popular youtuber recently made the first album that was fully produced using AI! In this video, she'll talk about her process and I'll help explain the technical details of how it all works (WaveNet style autoencoder, yay deep learning).

Code for this video: https://github.com/llSourcell/Music_Generation

Taryn's channel: https://www.youtube.com/watch?v=XUs6CznN8pw

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More learning resources: https://medium.com/@devinplatt/training-wavenet-with-tensorflow-on-aws-b0e2adeb204b https://deepmind.com/blog/wavenet-generative-model-raw-audio/ https://github.com/ibab/tensorflow-wavenet https://www.theverge.com/2017/8/27/16197196/taryn-southern-album-artificial-intelligence-interview

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Building an AI to beat pong using just the pixels of the screen as input with no hard-coded rules? Yes, its possible. We'll solve this using an approach called "Policy Gradients" which is even more popular than Q-learning. I'll show you how its done using a mix of animations, code, and theory. Let's beat pong!

Code (and challenge) for this week: https://github.com/llSourcell/policy_gradients_pong

Alex's Winning code: https://github.com/msoedov/q-learner

Aditya's Runner up code: https://github.com/avp1598/q_learning

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More learning resources: http://karpathy.github.io/2016/05/31/rl/ https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724 http://minpy.readthedocs.io/en/latest/tutorial/rl_policy_gradient_tutorial/rl_policy_gradient.html http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html http://kvfrans.com/simple-algoritms-for-solving-cartpole/ https://theneuralperspective.com/2016/11/25/reinforcement-learning-rl-policy-gradients-i/

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I hosted an event in a Long Beach mansion before NIPS (the big AI conference) started called TITS (Transformationally Intelligent Technologies Symposium). 150+ AI researchers from DeepMind, Google Brain, and other institutions showed up for the party. One of the invitees was George Hotz, a kid who built his own self driving car in his garage in Palo Alto. He also jailbroke the iphone first, as well as the PS3. He now runs comma.ai, a startup that builds self driving car technologies for everyday consumers. He's a really cool guy, and i whipped out my camera during party setup to get a brain download from him. Hope you like it!

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More about George: https://en.wikipedia.org/wiki/George_Hotz https://www.theverge.com/2017/7/7/15933554/george-hotz-hacking-self-driving-cars-comma-ai https://www.youtube.com/watch?v=aqdYbwY9vPU

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Can we train an AI to complete it's objective in a video game world without needing to build a model of the world before hand? The answer is yes using Q learning! I'll go through several use cases and show some python code of how Q learning works.

Code for this video: https://github.com/llSourcell/Q_Learning_Explained/

Adnan's Winning code: https://github.com/AdnanZahid/ReinforcementLearning

Alberto's runner up code: https://github.com/alberduris

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More learning resources: http://mnemstudio.org/path-finding-q-learning-tutorial.htm https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec23.pdf http://uhaweb.hartford.edu/compsci/ccli/projects/QLearning.pdf https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node19.html http://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html https://www.quora.com/How-is-policy-iteration-different-from-value-iteration http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf

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Andreas M Antonopoulos is one of the most well-known and well-respected figures in bitcoin. He is the author of two books: “Mastering Bitcoin”, published by O’Reilly Media and considered the best technical guide to bitcoin and “The Internet of Money”, a book about why bitcoin matters. He's also host of the "Let's Talk Bitcoin" podcast, a serial entrepreneur, and a public speaker. Oh, and he's my new friend we literally just met here in Helsinki since we were invited to speak at Slush. We will likely co-author a book at some point on blockchain + AI applications. He only had 30 minutes to chat with me today and i managed to cram in an 8 minute interview in the coffeeshop. Hope you like it!

More on Andreas: https://antonopoulos.com/ https://github.com/bitcoinbook/bitcoinbook (free copy of his book)

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We're going to program a virtual robot to do some house cleaning for us using a technique called monte carlo prediction. i'm going to explain what it is, how it works and how we can use it for reinforcement learning.

Code for this video: https://github.com/llSourcell/navigating_a_virtual_world_with_dynamic_programming

Justin's Winning code: https://github.com/wagonhelm/Value-Iteration

Sakcham's runner up code: https://github.com/sakchhams/pacman_ai

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More learning resources: https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec23.pdf http://uhaweb.hartford.edu/compsci/ccli/projects/QLearning.pdf https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node19.html http://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html https://www.quora.com/How-is-policy-iteration-different-from-value-iteration http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf

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Let's teach our AI how to get from point A to point B of a Frozen Lake environment in the most efficient way possible using dynamic programming. This is considered reinforcement learning and we'll trying two popular techniques (policy iteration and value iteration). We'll use OpenAI's Gym environment and pure python to do this.

Code for this video: https://github.com/llSourcell/navigating_a_virtual_world_with_dynamic_programming

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More learning resources: https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec23.pdf http://uhaweb.hartford.edu/compsci/ccli/projects/QLearning.pdf https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node19.html http://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html https://www.quora.com/How-is-policy-iteration-different-from-value-iteration http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf

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Welcome to my new reinforcement learning course! For the next 10 weeks we're going to go from the basics to the state of the art in this popular subfield of machine learning using video game environments as our testbed. RL is a huge reason DeepMind and OpenAI have been so successful thus far in creating world changing AI bots. Make sure to subscribe so you'll get updated with every new video I release. And don't worry if you don't understand policy iteration or value iteration just yet, I merely wanted to introduce these phrases in this video, next week i'm going to really dive into what these 2 methods look like programmatically.

Code for this video (with coding challenge): https://github.com/llSourcell/AI_for_video_games_demo

Syllabus for this course: https://github.com/llSourcell/AI_for_Video_Games_Syllabus

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More learning resources: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0 http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/ https://web.mst.edu/~gosavia/tutorial.pdf http://karpathy.github.io/2016/05/31/rl/ http://www.wildml.com/2016/10/learning-reinforcement-learning/ https://www.quora.com/What-are-some-good-tutorials-on-reinforcement-learning

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You Only Look Once - this object detection algorithm is currently the state of the art, outperforming R-CNN and it's variants. I'll go into some different object detection algorithm improvements over the years, then dive into YOLO theory and a programmatic implementation using Tensorflow!

Code for this video: https://github.com/llSourcell/YOLO_Object_Detection

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More learning resources: https://pjreddie.com/darknet/yolo/ https://timebutt.github.io/static/how-to-train-yolov2-to-detect-custom-objects/ http://machinethink.net/blog/object-detection-with-yolo/ https://github.com/pjreddie/darknet/wiki/YOLO:-Real-Time-Object-Detection https://github.com/KleinYuan/easy-yolo https://medium.com/@xslittlegrass/almost-real-time-vehicle-detection-using-yolo-da0f016b43de https://medium.com/diaryofawannapreneur/yolo-you-only-look-once-for-object-detection-explained-6f80ea7aaa1e

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Probabilistic programming languages are built to harness the predictive power of probability distributions. Instead of making them a feature, they use these distributions as primitives with their own set of operands that allow for the creation of stochastic control flows. Since the real world is full of uncertainty, this type of thinking is useful to help build better AI systems. I'll use Uber's newly released Pyro tool to demonstrate how they work.

Code for this video (with coding challenge): https://github.com/llSourcell/an_intro_to_probabilistic_programming

Shannon's winning code: https://github.com/DecentricCorp/Coval-Unspecified-Ml-Blockchain

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More learning resources: https://media.nips.cc/Conferences/2015/tutorialslides/wood-nips-probabilistic-programming-tutorial-2015.pdf http://probabilistic-programming.org/wiki/Home https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers https://www.oreilly.com/ideas/probabilistic-programming https://www.oreilly.com/learning/probabilistic-programming-from-scratch http://pyro.ai/

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Gotta train 'em all! Let's generate some new pokemon using the power of Generative Adversarial Networks. This is a newer deep learning technique invented by a researcher & friend of mine named Ian Goodfellow. Yann LeCunn called it the coolest idea in the past 2 decades. I'll explain how it works, some more recent improvements, then we'll go through the code.

Code for this video: https://github.com/llSourcell/Pokemon_GAN

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More learning resources: https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners https://www.analyticsvidhya.com/blog/2017/06/introductory-generative-adversarial-networks-gans/ https://github.com/uclaacmai/Generative-Adversarial-Network-Tutorial http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ https://www.slideshare.net/ThomasDaSilvaPaula/a-very-gentle-introduction-to-generative-adversarial-networks-aka-gans-71614428 https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39

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Let's take a step back from the technical details for a second. Before you actually start working on a project/job/career, how are you supposed to decide what it is you want to work on? I'll go over 8 steps you can take today to find meaningful work in your life, and give some personal advice from my own life experiences.

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More learning resources: https://www.consciouslifestylemag.com/meaningful-work-the-eight-keys/ https://www.linkedin.com/pulse/10-truths-finding-meaningful-work-adam-smiley-poswolsky/ https://www.forbes.com/sites/nextavenue/2016/10/04/the-5-key-questions-to-find-meaningful-work/#2d307b606c96 https://greatergood.berkeley.edu/article/item/six_tips_for_making_the_leap_to_meaningful_work

8 Steps:

Allow yourself to be confused.
Take quality time to reflect on yourself.
Practice Self-Love.
Rapid experimentation.
Create a goal plan.
Learn to say no to everything but your goals.
Practice Self Discipline.
Be open to feedback.

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Geoffrey Hinton (who popularized backpropagation in the 80s) recently published his long-awaited paper on "Capsule Networks" - which provide state of the art classification accuracy on the MNIST dataset. In this video I'll go over how Convolutional networks work, some improvements to them over the years, then i'll go over capsule networks in both theory and code.

Code for this video: https://github.com/llSourcell/capsule_networks

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More learning resources: https://www.youtube.com/watch?v=FTr3n7uBIuE https://www.youtube.com/watch?v=cAICT4Al5Ow https://www.youtube.com/watch?v=qVwm-9P609I https://www.youtube.com/watch?v=LodC7Zm3X8Q https://github.com/naturomics/CapsNet-Tensorflow https://www.reddit.com/r/MachineLearning/comments/78zvc4/r_171009829_dynamic_routing_between_capsules/

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There's been way too many fear-mongering news articles around the latest version of DeepMind's AlphaGo. Let's set the record straight, AlphaGo is an incredible technology and it's not terrifying at all. I'll go over the technical details of how AlphaGo really works; a mixture of deep learning and reinforcement learning.

Code that replicates AlphaGo: https://github.com/llSourcell/alphago_demo

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More learning resources: https://medium.com/@karpathy/alphago-in-context-c47718cb95a5 https://www.tastehit.com/blog/google-deepmind-alphago-how-it-works/ https://machinelearnings.co/understanding-alphago-948607845bb1 https://deepmind.com/research/alphago/ https://www.dcine.com/2016/01/28/alphago/ https://www.slideshare.net/ShaneSeungwhanMoon/how-alphago-works https://www.youtube.com/watch?v=qWcfiPi9gUU

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DeepMind released an optimization strategy that could become the most popular approach for training very deep neural networks, even more so than backpropagation. I don't think it got enough love, so i'm going to explain how it works myself and why i think it's so cool. Already know how backpropagation works? Skip to 14:10

Code for this video: https://github.com/llSourcell/synthetic_gradients_explained

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More learning resources: https://iamtrask.github.io/2017/03/21/synthetic-gradients/ https://arxiv.org/abs/1703.00522 https://deepmind.com/blog/decoupled-neural-networks-using-synthetic-gradients/

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More and more apps will start using smart contract technology to enable never before possible features. We're going to build a smart contract called "proof of existence" that acts as a digital notary for any document using the Ethereum blockchain.

Code for this video: https://github.com/llSourcell/proof_of_existence_demo

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More learning resources: https://www.slideshare.net/intrins1k/ethereum-meetup-presentation-01042017-70716809 https://www.youtube.com/watch?v=R_CiemcFKis https://auth0.com/blog/an-introduction-to-ethereum-and-smart-contracts-part-2/ https://ethereumdev.io/ https://ethereum.gitbooks.io/frontier-guide/content/writing_contract.html http://hypernephelist.com/2016/06/01/deploying-my-first-smart-contract.html https://blog.cloudboost.io/ethereum-smart-contracts-in-a-nutshell-for-hackers-64f357715791 http://www.techracers.com/smart-contract-solidity http://ecomunsing.com/tutorial-controlling-ethereum-with-python

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Help me Interview Elon Musk! Retweet this link:

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Let's make it happen! 67 Questions featuring Elon Musk at SpaceX Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Let's build a decentralized ticket service using Ethereum! Ethereum is the 2nd biggest cryptocurrency in market cap behind Bitcoin and offers a Turing-complete blockchain. Using Ethereum + IPFS, developers can build powerful decentralized applications, and this offers novelty in a somewhat saturated market for app developers. Let's get started!

Code for this video: https://github.com/llSourcell/ethereum_demo

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More learning resources; https://www.youtube.com/watch?v=8jI1TuEaTro https://ethereum.org/greeter https://dappsforbeginners.wordpress.com/ https://ethereum.stackexchange.com/questions/5952/ethereum-tutorial-for-beginners http://truffleframework.com/tutorials/ethereum-overview https://ethereumbuilders.gitbooks.io/guide/content/en/solidity_tutorials.html https://blockgeeks.com/guides/how-to-learn-solidity/

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I'm re-uploading this video since my last one could only be played on Desktop devices because of a copyright claim from UMG. Trust is at the core of every human interaction. I'm going to describe what trust means to me, the evolution of human systems to facilitate trust, and where we're going.

Zacharias' Winning Code: https://github.com/ZachisGit/ipfs-arxiv

Anup's Runner up Code: https://github.com/Ghatage/horcrux

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More learning resources: https://medium.com/@BlockByBlock/the-decentralized-autonomous-organization-dao-5e80cfe8c993 https://medium.com/dash-for-newbies/how-to-get-funded-by-a-decentralized-autonomous-organization-4d2430572bcb https://medium.com/practical-blockchain/decentralized-autonomous-organizations-overview-2e752e194c5b https://medium.freecodecamp.org/token-network-effects-a-new-business-model-for-a-decentralized-web-6cde8b4e862 https://www.fastcompany.com/3047462/the-humans-who-dream-of-companies-that-wont-need-them

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Credits for the intro song go to Universal Music Group Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Trust is at the core of every human interaction. I'm going to describe what trust means to me, the evolution of human systems to facilitate trust, and where we're going.

Only available on desktop devices

Zacharias' Winning Code: https://github.com/ZachisGit/ipfs-arxiv

Anup's Runner up Code: https://github.com/Ghatage/horcrux

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More learning resources: https://medium.com/@BlockByBlock/the-decentralized-autonomous-organization-dao-5e80cfe8c993 https://medium.com/dash-for-newbies/how-to-get-funded-by-a-decentralized-autonomous-organization-4d2430572bcb https://medium.com/practical-blockchain/decentralized-autonomous-organizations-overview-2e752e194c5b https://medium.freecodecamp.org/token-network-effects-a-new-business-model-for-a-decentralized-web-6cde8b4e862 https://www.fastcompany.com/3047462/the-humans-who-dream-of-companies-that-wont-need-them

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Is blockchain + AI a winning combo? Yes! They are complementary technologies, and knowing how both work will make you a much more powerful developer. Artificial Intelligence can use the power of the blockchain to audit data, add incentives to its goals, and even create new types of meritocratic organizations. In this video, i'll talk about how they can both work together, code out the proof of work algorithm in python, then talk about a few other consensus algorithms at a high level.

Code for this video: https://github.com/llSourcell/blockchain_consensus

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More Learning Resources: https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984 https://www.topbots.com/combination-ai-blockchain-revolutionize-10-industries/ https://blog.oceanprotocol.com/from-ai-to-blockchain-to-data-meet-ocean-f210ff460465 https://www.slideshare.net/bicalabs/artificial-intelligence-blockchain-synergy https://blog.ethereum.org/2014/05/06/daos-dacs-das-and-more-an-incomplete-terminology-guide/ https://bitcoinmagazine.com/articles/bootstrapping-a-decentralized-autonomous-corporation-part-i-1379644274/ https://www.wired.com/2016/06/50-million-hack-just-showed-dao-human/

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Towards the Permanent Web! HTTP has served us well, but its time to upgrade the way the Internet works. IPFS provides a solution for the ills of HTTP. It content addresses data instead of location addressing it, and provides more bandwidth, better latency, and more resiliency. We'll build a simple video streaming web app using IPFS!

Code for this video: https://github.com/llSourcell/IPFS_Demo

Tushar's Winning Code: https://github.com/OpenMined/PySyft/pull/268/commits

Rohan's Runner-up Code: https://github.com/OpenMined/PySyft/pull/273

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More learning Resources: https://ipfs.io/docs/examples/ https://flyingzumwalt.gitbooks.io/decentralized-web-primer/content/files-on-ipfs/ https://github.com/INFURA/tutorials/wiki/Introduction-to-IPFS https://www.youtube.com/watch?v=jONZtXMu03w&t=341s https://ethereum.stackexchange.com/questions/7664/how-can-we-integrate-ipfs-with-ethereum-in-dapps https://mlgblockchain.com/intro-ipfs.html https://medium.com/@ConsenSys/an-introduction-to-ipfs-9bba4860abd0

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Back-propagation is fundamental to deep learning. Hinton (the inventor) recently said we should "throw it all away and start over". What should we do? I'll describe how back-propagation works, how its used in deep learning, then give 7 interesting research directions that could overtake back-propagation in the near term.

Code for this video: https://github.com/llSourcell/7_Research_Directions_Deep_Learning

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More learning resources: https://www.youtube.com/watch?v=q555kfIFUCM https://www.youtube.com/watch?v=h3l4qz76JhQ https://www.youtube.com/watch?v=vOppzHpvTiQ https://deeplearning4j.org/deepautoencoder https://deeplearning4j.org/glossary https://www.reddit.com/r/MachineLearning/comments/70e4ex/n_hinton_says_we_should_scrap_back_propagation/ https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ http://kvfrans.com/generative-adversial-networks-explained/

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OpenMined is a community focused on building technology for decentralized ownership of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way. We'll cover deep learning, federated learning, homomorphic encryption, and blockchain smart contracts!

Code for this video: https://github.com/llSourcell/OpenMined_demo

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More learning resources: https://openmined.slack.com/ http://openmined.org/ https://github.com/OpenMined/Docs https://research.googleblog.com/2017/04/federated-learning-collaborative.html https://www.youtube.com/playlist?list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 https://www.youtube.com/watch?v=LZEHOlZY2To&t=8s https://www.youtube.com/watch?v=gSQXq2_j-mw

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Web 3.0 is here! Welcome to the dark side of web and mobile development (in a good way). Lets talk about how we can use blockchains, distributed hash tables, and peer to peer protocols to create decentralized applications!

Code for this video: https://github.com/llSourcell/Your_First_Decentralized_Application

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More learning resources: https://www.safaribooksonline.com/library/view/decentralized-applications/9781491924532/ch01.html https://github.com/DavidJohnstonCEO/DecentralizedApplications https://coinsutra.com/dapps-decentralized-applications/ https://medium.com/@mvmurthy/full-stack-hello-world-voting-ethereum-dapp-tutorial-part-1-40d2d0d807c2 https://blockgeeks.com/guides/dapps-the-decentralized-future/ https://dappsforbeginners.wordpress.com/tutorials/your-first-dapp/ https://ethereum.stackexchange.com/questions/122/how-to-create-a-dapp-from-scratch-on-ethereum https://blog.coinbase.com/app-coins-and-the-dawn-of-the-decentralized-business-model-8b8c951e734f

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Credits to Mahesh Murthy for the code and inspiration: http://www.zastrin.com

Let's talk Brain Computer Interfaces. BCI's are the next step in human evolution, they are the inevitable upgrade we'll need after the age of smartphones. If done right, they can help us fulfill our wildest fantasies. We'll be able to learn anything, experience anything, and be anywhere in seconds. I'll discuss the philosophical, theoretical, and technical aspects behind the idea.

Code for this video: https://github.com/llSourcell/Brain_Computer_Interface

Tinkula's Winning code: https://github.com/tterava/Mandelbrot

이정환 runner-up code: https://github.com/jhGitHub009/cuda_c-

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More learning resources: https://sccn.ucsd.edu/wiki/Introduction_To_Modern_Brain-Computer_Interface_Design https://www.youtube.com/watch?v=Rv1ckJVrRqo http://videolectures.net/mlss06au_muller_bci/ https://www.mepits.com/tutorial/173/Biomedical/BCI---Brain-Computer-Interface https://waitbutwhy.com/2017/04/neuralink.html https://gizmodo.com/scientists-just-invented-the-neural-lace-1711540938

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If you can parallelize your code by harnessing the power of the GPU, I bow to you. GPU code is usually abstracted away by by the popular deep learning frameworks, but knowing how it works is really useful. CUDA is the most popular of the GPU frameworks so we're going to add two arrays together, then optimize that process using it. I love CUDA!

Code for this video: https://github.com/llSourcell/An_Introduction_to_GPU_Programming

Alberto's Winning Code: https://github.com/alberduris/SirajsCodingChallenges/tree/master/Stock%20Market%20Prediction

Hutauf's runner-up code: https://github.com/hutauf/Stock_Market_Prediction

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More learning resources: http://supercomputingblog.com/cuda-tutorials/ http://www.nvidia.com/docs/IO/116711/sc11-cuda-c-basics.pdf https://devblogs.nvidia.com/parallelforall/even-easier-introduction-cuda/ https://developer.nvidia.com/cuda-education-training https://llpanorama.wordpress.com/cuda-tutorial/ https://www.udacity.com/course/intro-to-parallel-programming--cs344 http://lorenabarba.com/gpuatbu/Program_files/Cruz_gpuComputing09.pdf http://cuda-programming.blogspot.nl/p/tutorial.html https://www.cc.gatech.edu/~vetter/keeneland/tutorial-2011-04-14/02-cuda-overview.pdf

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No, Nvidia did not pay me to make this video lol. I just love CUDA.

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I'm going to go through the steps necessary to install and run the StarCraft II Environment that DeepMind recently open-sourced! I'll discuss DeepMind's RL history, the configuration steps, and then we'll run a pre-trained Deep Q model at the end that will complete a mini-game.

Code for this video: https://github.com/llSourcell/A-Guide-to-DeepMinds-StarCraft-AI-Environment

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More learning resources: https://arxiv.org/abs/1708.04782 http://chris-chris.ai/2017/08/30/pysc2-tutorial1/ https://github.com/deepmind/pysc2 https://github.com/Blizzard/s2client-proto https://deepmind.com/blog/deepmind-and-blizzard-open-starcraft-ii-ai-research-environment/ http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/ https://blog.openai.com/openai-baselines-dqn/

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Can we predict the price of Microsoft stock using Machine Learning? We'll train the Random Forest, Linear Regression, and Perceptron models on many years of historical price data as well as sentiment from news headlines to find out!

Code for this video: https://github.com/llSourcell/Stock_Market_Prediction

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More learning resources: https://www.quantinsti.com/blog/machine-learning-trading-predict-stock-prices-regression/ https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02 https://iknowfirst.com/rsar-machine-learning-trading-stock-market-and-chaos https://www.udacity.com/course/machine-learning-for-trading--ud501 https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://www.linkedin.com/pulse/deep-learning-stock-price-prediction-explained-joe-ellsworth

If you're wondering why my voice sounds weird, it's because i was down with Traveler's Diarrhea from my recent trip to India. It's such a debilitating sickness, but the show must go on. And yes, thankfully I'm better now :)

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These are 10 strategies I use to learn advanced concepts as fast as possible! I'm going to explain each one, and give some examples of what I mean. No coding challenge this week, hope its helpful!

10 Strategies below:

Find a reason to learn
Start with the simplest explanations
Create a set of small, achievable goals
Set Deadlines
Maintain a flow state
Let your curiosity guide your learning path
Spend 1/3 of your time researching & 2/3 doing
Take notes by hand
Dont multitask
Maintain your Health

(yes, i mis-numbered some of them in the video accidentally)

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Apple's newly released CoreML framework makes it super simple for developers to run inference of pre-trained models on their iOS devices. Let's talk about what the development workflow and APIs look like, then we'll build an SMS spam classifier using it.

Code for this video: https://github.com/llSourcell/A_guide_to_coreML

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More learning resources: https://developer.apple.com/documentation/coreml https://developer.apple.com/videos/play/wwdc2017/703/ https://www.raywenderlich.com/164213/coreml-and-vision-machine-learning-in-ios-11-tutorial http://alexsosn.github.io/ml/2017/06/09/Core-ML-will-not-Work-for-Your-App.html https://medium.com/towards-data-science/welcoming-core-ml-8ba325227a28 https://www.bignerdranch.com/blog/machine-learning-in-ios-using-core-ml/

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I crowdsourced questions on Twitter, and picked 67 of them for an interview with myself! I had my friend Daniel Rigberg ask the questions and film me.

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Resources I've mentioned:

Deep Learning Book: http://www.deeplearningbook.org/

My playlists/courses: https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists

Cormac McCarthy's Book: https://www.amazon.com/Road-Cormac-McCarthy/dp/0307387895

Udacity Nanodegree: https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Andrew Ng's Course: https://www.coursera.org/specializations/deep-learning?siteID=SAyYsTvLiGQ-ZwaOIEdDzM4pwFY43MoUFQ&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ

ML Subreddit: https://www.reddit.com/r/machinelearning

Filecoin: http://filecoin.io/

OpenMined: http://openmined.org/

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Daniels website: http://www.danielrigberg.com/

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The Road

Let's cut through the hype and understand Initial Coin Offerings (ICOs) by creating one ourselves programmatically! We'll first learn about Bitcoin & Ethereums architecture to prepare us for the smart contract creation process. Our DemoCoin ICO will be built using tools from the Ethereum developer ecosystem.

Code for this video: https://github.com/llSourcell/what_is_an_initial_coin_offering

Alberto's Winning Code: https://github.com/alberduris/The_Math_of_Intelligence/tree/master/Week10

Eric's 2nd place Code: https://github.com/EricAlcaide/Math_of_Intelligence/tree/master/Quantum_Computing

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More learning resources: https://medium.com/startup-grind/hack-your-funding-with-an-initial-coin-offering-2a2a0614bddf https://blog.zeppelin.solutions/how-to-create-token-and-initial-coin-offering-contracts-using-truffle-openzeppelin-1b7a5dae99b6 https://bitsonblocks.net/2017/04/25/a-gentle-introduction-to-initial-coin-offerings-icos/ https://blockchainhub.net/ico-initial-coin-offerings/ https://medium.com/@mvmurthy/full-stack-hello-world-voting-ethereum-dapp-tutorial-part-1-40d2d0d807c2 https://medium.com/@ConsenSys/a-101-noob-intro-to-programming-smart-contracts-on-ethereum-695d15c1dab4 https://www.ethereum.org/greeter

I wrote a book on this stuff last year (most of the code is now deprecated since the space moves very fast, but the theories still hold true) so I'm very excited to see this space finally coming to fruition: http://shop.oreilly.com/product/0636920039334.do

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Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool.

Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams

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More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/

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Quantum Computing offers hope for computing progress as we approach the limits of transistor density on silicon hardware. We're going to talk about the theory behind them then build our own quantum algorithm using IBM's Quantum API! This is the last episode of this series.

Code for this video: https://github.com/llSourcell/quantum_computing

Noah's Winning code: https://github.com/NoahLidell/math-of-intelligence/tree/master/q_learning

jhGitHub009's Runner Up code: https://github.com/jhGitHub009/Game_bot_DQN

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More learning resources: https://people.cs.umass.edu/~strubell/doc/quantum_tutorial.pdf https://physics.stackexchange.com/questions/3390/can-anybody-provide-a-simple-example-of-a-quantum-computer-algorithm http://michaelnielsen.org/blog/quantum-computing-for-everyone/ https://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer http://www.quantumplayground.net/#/home https://www.research.ibm.com/ibm-q/

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Special thanks to TED & Kurzgesagt for the animation clips Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/

Evolutionary/genetic algorithms are somewhat of a mystery to many in the machine learning discipline. You don't see papers regularly published using them but they are a really fascinating subfield and in this video, we're going to use a genetic algorithm to improve invaders in a space invaders game!

Code for this video: https://github.com/llSourcell/Evolutionary_Space_Invaders

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More learning resources: http://www.ai-junkie.com/ga/intro/gat1.html http://www.tutorialspoint.com/genetic_algorithms/ http://www.theprojectspot.com/tutorial-post/creating-a-genetic-algorithm-for-beginners/3 http://www.obitko.com/tutorials/genetic-algorithms/ http://www-cs-students.stanford.edu/~jl/Essays/ga.html http://www.alanzucconi.com/2016/04/06/evolutionary-coputation-1/

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We're going to replicate DeepMind's Deep Q Learning algorithm for Super Mario Bros! This bot will be able to play a bunch of different video games by using reinforcement learning. This is the first video in this series that uses libraries (Keras & Gym) because if it didn't, the code would be way too long for a short video. I'll make a longer, in-depth version without libraries soon.

Code for this video: https://github.com/llSourcell/deep_q_learning

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More learning resources: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0 http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/ http://karpathy.github.io/2016/05/31/rl/ https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html https://keon.io/deep-q-learning/ http://www0.cs.ucl.ac.uk/staff/d.silver/web/Resources_files/deep_rl.pdf http://mnemstudio.org/path-finding-q-learning-tutorial.htm

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Recurrent Networks can be improved to remember long range dependencies by using whats called a Long-Short Term Memory (LSTM) Cell. Let's build one using just numpy! I'll go over the cell components as well as the forward and backward pass logic.

Code for this video: https://github.com/llSourcell/LSTM_Networks

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More learning resources: https://www.youtube.com/watch?v=ftMq5ps503w https://www.youtube.com/watch?v=cdLUzrjnlr4 https://www.youtube.com/watch?v=hWgGJeAvLws http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

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I ask 67 questions to Oxford Scholar and AI researcher Andrew Trask as we go for a walk through Granary Square in London, England. Trask is a PhD student at Oxford University where he researches Deep Learning approaches with special emphasis on human language. We worked together on my Udacity deep learning nanodegree and I have a great deal of respect for his technical storytelling ability.

Please hit that subscribe button if you liked this interview!

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Some of Trask's work here: https://www.manning.com/books/grokking-deep-learning https://scholar.google.com/citations?user=2Ajxf1sAAAAJ&hl=en https://iamtrask.github.io/

Here's his Linkedin: https://www.linkedin.com/in/andrew-trask-545a6663/

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Generative Models are insanely cool! They help create never before seen data. We'll go over the mathematical difference between discriminative and generative models, talk about a few types, then dive into a basic one called Latent Dirichlet Allocation to generate a set of topics for some news articles.

Code for this video: https://github.com/llSourcell/Latent_Dirichlet_Allocation

Hammad's Winning Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Bayesian%20Linear%20Regression

Noah's Runner up code: https://github.com/NoahLidell/math-of-intelligence/tree/master/hyperparameter_optimization

Carykh's channel: https://www.youtube.com/user/carykh

More learning resources: https://www.youtube.com/watch?v=qCA1Dk_Ih_c&t=383s http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/ http://ai.stanford.edu/~ang/papers/jair03-lda.pdf https://rstudio-pubs-static.s3.amazonaws.com/79360_850b2a69980c4488b1db95987a24867a.html https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation https://blog.bigml.com/2016/11/16/introduction-to-topic-models/ https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/ https://github.com/blei-lab/onlineldavb

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We're going to predict customer churn using a clustering technique called the Gaussian Mixture Model! This is a probability distribution that consists of multiple Gaussian distributions, very cool. I also have something important but unrelated to say in the beginning of the video.

Code for this video: https://github.com/llSourcell/Gaussian_Mixture_Models

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More learning resources: http://yulearning.blogspot.nl/2014/11/einsteins-most-famous-equation-is-emc2.html http://web.iitd.ac.in/~sumeet/GMM_said_crv10_tutorial.pdf https://brilliant.org/wiki/gaussian-mixture-model/ http://www.vlfeat.org/overview/gmm.html http://www.informatica.uniroma2.it/upload/2009/IM/mixture-tutorial.pdf http://cs.nyu.edu/~dsontag/courses/ml12/slides/lecture21.pdf http://statweb.stanford.edu/~tibs/stat315a/LECTURES/em.pdf

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Hyperparameters are the magic numbers of machine learning. We're going to learn how to find them in a more intelligent way than just trial-and-error. We'll go over grid search, random search, and Bayesian Optimization. I'll also cover the difference between Bayesian and Frequentist probability.

Code for this video: https://github.com/llSourcell/hyperparameter_optimization_strategies

Noah's Winning Code: https://github.com/NoahLidell/math-of-intelligence/tree/master/probability_theory

Hammad's Runner-up Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Naive%20Bayes%20Classifier

More learning resources: https://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf https://thuijskens.github.io/2016/12/29/bayesian-optimisation/ https://jmhessel.github.io/Bayesian-Optimization/ https://arimo.com/data-science/2016/bayesian-optimization-hyperparameter-tuning/ https://dhnzl.files.wordpress.com/2016/12/fuzzymad2016_bo_pdf.pdf http://blog.revolutionanalytics.com/2016/06/bayesian-optimization-of-machine-learning-models.html https://www.youtube.com/watch?v=cWQDeB9WqvU https://nlpers.blogspot.nl/2014/10/hyperparameter-search-bayesian.html http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html

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Thanks to Veritasium (bayesian animation) & Angela Schoellig (drone clip) Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This is one of the most used machine learning models ever. Random Forests can be used for both regression and classification, and our use case will be to assess whether someone is credible or not by analyzing their financial history!

DL nanodegree open for another round! we'll pick one random student that signs up in next 24 hrs to collab w/ me one-on-one on a DL music project https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Code for this video: https://github.com/llSourcell/random_forests

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More learning resources: https://ujjwalkarn.me/2016/05/30/a-curated-list-of-python-tutorials-for-data-science-nlp-and-machine-learning/ https://www.coursera.org/learn/machine-learning-data-analysis/lecture/eTO92/building-a-random-forest-with-python https://github.com/kevin-keraudren/randomforest-python http://kldavenport.com/pure-python-decision-trees/ http://blog.yhat.com/posts/random-forests-in-python.html https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/ http://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/ http://machinelearningmastery.com/implement-random-forest-scratch-python/

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We'll build a Spam Detector using a machine learning model called a Naive Bayes Classifier! This is our first real dip into probability theory in the series; I'll talk about the types of probability, then we'll use Bayes Theorem to help us build our classifier.

Code for this video: https://github.com/llSourcell/naive_bayes_classifier/

Hammad's Winning Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Principal%20Component%20Analysis

Kristian's Runner up Code: https://github.com/kwichmann/PCA_and_autoencoders

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More Learning Resources: http://machinelearningmastery.com/naive-bayes-tutorial-for-machine-learning/ http://blog.datumbox.com/machine-learning-tutorial-the-naive-bayes-text-classifier/ http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/ https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/ https://www.youtube.com/watch?v=psHrcSacU9Y https://hackernoon.com/how-to-build-a-simple-spam-detecting-machine-learning-classifier-4471fe6b816e https://www.autonlab.org/tutorials/naive.html

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Recurrent networks let us learn from sequential data (time series, music, audio, video frames, etc ). We're going to build one from scratch in numpy (including backpropagation) to generate a sequence of words in the style of Franz Kafka.

Code for this video: https://github.com/llSourcell/recurrent_neural_network

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More learning resources: https://www.youtube.com/watch?v=hWgGJeAvLws https://www.youtube.com/watch?v=cdLUzrjnlr4 https://medium.freecodecamp.org/dive-into-deep-learning-with-these-23-online-courses-bf247d289cc0 http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ https://deeplearning4j.org/lstm.html http://karpathy.github.io/2015/05/21/rnn-effectiveness/

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My name is Siraj Raval. I'll teach you how to use machine learning to build literally anything you can dream up. Generating entirely new virtual worlds, self driving cars, games, music, art, predicting time series data, anything is possible. This song is a parody of the Kanye West + Daft Punk song "Stronger".

Special thanks to these people who helped me with the video: Daniel Rigberg: http://www.danielrigberg.com/ (engineer + created the backing track) Shriya Nevatia: https://www.linkedin.com/in/shriyanevatia (AI engineer) Mimee Xu: https://www.facebook.com/mimee.bae (AI engineer) Teya Ena: https://twitter.com/TeyaEna (Growth hacker) Gwen Tegels: https://www.facebook.com/gwen.tegels Justin Alvey: https://www.linkedin.com/in/justinalvey (CTO Birdi) Sophie Du Palais: https://angel.co/sophie-du-palais (Business Development) Pascal Van Kooten: https://twitter.com/kootenpv (AI engineer)

Available on Spotify: https://open.spotify.com/track/3fBbmO79x0KjhbQX3hqIo2

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More info on generating words (LSTMs) http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Lyrics:

Build it, train it, test it, Makes it denser, deeper, faster, smarter!

[played in background, continuously:] Optimize it, make it better, Adding layers makes it smarter classifier, generator, Accurate with lower error,

I-I-I got to read papers To try and make me smarter i train my models in the cloud now Cause my laptop takes longer I parse through data like a boss now back then my code was wronger Subscribe if ya wanna learn now lets spread this AI power

Lets train it right now Lets train it right now

Lets make bots tonight and make music like Mozart tonight And paint artwork on a cross tonight i’ve waited for this software all my life Just give it some data and watch it explore step back, clap and give an Encore It trained, on forty cores, does Raycer make GPUs anymore? my game bot started off aimless But now its like god in the matrix My chat bot speaks once I train it i worked so damn hard to make this it’ll generate words that it predicts analyze sentiment, then test it this def is the shit to Invest in New data? Wizard train it, then test it, commit !!

i-i-i got to read papers To try and make me smarter i train my models in the cloud now Cause my laptop takes longer I parse through data like a boss now back then my code was wronger Subscribe iff ya wanna learn now lets spread this AI power

Lets train it right now Lets train it right now

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Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis.

Code for this video: https://github.com/llSourcell/Dimensionality_Reduction

Ong's Winning Code: https://github.com/jrios6/Math-of-Intelligence/tree/master/4-Self-Organizing-Maps

Hammad's Runner up Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Self%20Organizing%20Maps%20for%20Data%20Visualization

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I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw

More learning resources: https://plot.ly/ipython-notebooks/principal-component-analysis/ https://www.youtube.com/watch?v=lrHboFMio7g https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ http://setosa.io/ev/principal-component-analysis/ http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/

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Convolutional Networks allow us to classify images, generate them, and can even be applied to other types of data. We're going to build one in numpy that can classify and type of alphanumeric character and it will run in a Flask web app.

Code for this video: https://github.com/llSourcell/Convolutional_neural_network

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More learning resources: https://github.com/dorajam/Convolutional-Network https://beckernick.github.io/neural-network-scratch/ https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/ http://cs231n.github.io/convolutional-networks/ http://deeplearning.net/tutorial/lenet.html https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ https://www.youtube.com/watch?v=q555kfIFUCM&t=31s

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Have you ever wondered what the math behind neural networks looks like? What gives them such incredible power? We're going to cover 4 different neural networks in this video to develop an intuition around their basic principles (2 feedforward networks, 1 recurrent network, and a self-organizing map). Prepare yourself, deep learning is coming.

Code for this video (with coding challenge): https://github.com/llSourcell/neural_networks

Hammad's winning code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Regularization%20in%20Linear%20Regression

Ong's runner-up code: https://github.com/jrios6/Math-of-Intelligence/tree/master/3-Regularization

More learning resources: https://www.youtube.com/watch?v=h3l4qz76JhQ http://www.ai-junkie.com/ann/som/som1.html http://iamtrask.github.io/2015/07/12/basic-python-network/ https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

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Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.

Code for this video: https://github.com/llSourcell/k_means_clustering

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More learning resources: http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html http://people.revoledu.com/kardi/tutorial/kMean/ https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html http://mnemstudio.org/clustering-k-means-example-1.htm https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html

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We're going to explore why the concept of vectors is so important in machine learning. We'll talk about how they are used to represent both data and models. Get ready for some Linear Algebra!

Code for this video (with challenge): https://github.com/llSourcell/Vectors_Linear_Algebra/tree/master

Vishnu's Winning Code: https://github.com/Sri-Vishnu-Kumar-K/MathOfIntelligence/blob/master/second_order_optimization_newtons_method/second_order_optimization.py

Hammad's Runner-up Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Newtons%20Method.ipynb

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More learning resources: http://mathworld.wolfram.com/VectorNorm.html http://www.math.usm.edu/lambers/mat610/sum10/lecture2.pdf https://www.youtube.com/watch?v=tXCqr2UsbWQ https://stackoverflow.com/questions/38379905/what-is-vector-in-terms-of-machine-learning http://www.chioka.in/differences-between-the-l1-norm-and-the-l2-norm-least-absolute-deviations-and-least-squares/ https://www.quora.com/What-is-the-difference-between-L1-and-L2-regularization

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We're going to use logistic regression to predict if someone has diabetes or not given 3 body metrics! We'll use Newton's method to help us optimize the model. We'll use a bit from all the mathematical disciplines i've mentioned (calculus, linear algebra, probability theory, statistics).

Code for this video: https://github.com/llSourcell/logistic_regression_newtons_method

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More learning resources: http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex4/ex4.html https://www.youtube.com/watch?v=X-7sA83PjPM https://www.youtube.com/watch?v=TuttBDdbls8 https://rstudio-pubs-static.s3.amazonaws.com/160015_b192ca9855e84b57814e785ebd034a5e.html https://www.r-bloggers.com/machine-learning-ex4-logistic-regression-and-newtons-method/ https://statacumen.com/teach/SC1/SC1_11_LogisticRegression.pdf

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Gradient Descent and its variants are very useful, but there exists an entire other class of optimization techniques that aren't as widely understood. We'll learn about second order method variants, how they compare to first order methods, and implement our own in Python.

Code for this video (with challenge): https://github.com/llSourcell/Second_Order_Optimization_Newtons_Method

Alberto's Winning Code: https://github.com/alberduris

Ivan's Runner up Code: https://github.com/PiaFraus

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Course Syllabus: https://github.com/llSourcell/The_Math_of_Intelligence

More learning resources: https://web.stanford.edu/class/msande311/lecture13.pdf https://www.cs.toronto.edu/~hinton/csc2515/notes/lec6tutorial.pdf https://www.quora.com/In-mathematical-optimization-problems-the-first-derivative-is-often-used-Why-not-the-second-or-higher-order-derivatives https://en.wikipedia.org/wiki/Newton%27s_method_in_optimization https://www.youtube.com/watch?v=28BMpgxn_Ec&t=444s https://www.youtube.com/watch?v=42zJ5xrdOqo&t=438s

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Support Vector Machines are a very popular type of machine learning model used for classification when you have a small dataset. We'll go through when to use them, how they work, and build our own using numpy. This is part of Week 1 of The Math of Intelligence. This is a re-recorded version of a video I just released a day ago (the audio/video quality is better in this one)

Code for this video: https://github.com/llSourcell/Classifying_Data_Using_a_Support_Vector_Machine

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Course Syllabus: https://github.com/llSourcell/The_Math_of_Intelligence

Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

More Learning resources: https://www.analyticsvidhya.com/blog/2015/10/understaing-support-vector-machine-example-code/ http://www.robots.ox.ac.uk/~az/lectures/ml/lect2.pdf http://machinelearningmastery.com/support-vector-machines-for-machine-learning/ http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf http://www.statsoft.com/Textbook/Support-Vector-Machines https://www.youtube.com/watch?v=_PwhiWxHK8o

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Welcome to The Math of Intelligence! In this 3 month course, we'll cover the most fundamental math concepts in Machine Learning. In this first lesson, we'll go over a very popular optimization technique called gradient descent to help us predict how many calories a cyclist would burn given just their distance traveled. We'll also follow the story of 2 data scientists as they attempt to find the Higgs-Boson (God particle) via anomaly detection. No collaborations, this is an independent course.

Code for this video (with challenge details): https://github.com/llSourcell/Intro_to_the_Math_of_intelligence

TypicalHog's winning code: https://github.com/TypicalHog/THCrypt

Syllabus for this course: https://github.com/llSourcell/The_Math_of_Intelligence

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More learning resources: http://machinelearningmastery.com/linear-regression-tutorial-using-gradient-descent-for-machine-learning/ https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/ https://www.coursera.org/learn/machine-learning/lecture/kCvQc/gradient-descent-for-linear-regression http://cs229.stanford.edu/notes/cs229-notes1.pdf http://blog.hackerearth.com/gradient-descent-algorithm-linear-regression https://www.r-bloggers.com/linear-regression-by-gradient-descent/ https://www.youtube.com/watch?v=XdM6ER7zTLk&t=1650s

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Let's create an Android app that uses a pre-trained Tensorflow image classifier for MNIST digits to recognize what the user draws on the screen. We'll use Android Studio and the gradle build system to make it happen.

Code for this video: https://github.com/llSourcell/A_Guide_to_Running_Tensorflow_Models_on_Android

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More learning resources: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android https://www.tensorflow.org/mobile/ https://www.oreilly.com/learning/tensorflow-on-android https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc https://venturebeat.com/2017/05/17/android-launches-tensorflow-lite-for-mobile-machine-learning/ http://nilhcem.com/android/custom-tensorflow-classifier https://medium.com/@daj/creating-an-image-classifier-on-android-using-tensorflow-part-1-513d9c10fa6a https://omid.al/posts/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html

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In this video, you'll learn how the blockchain works. You won't find this data structure in your computer science textbooks yet, but it will soon underpin the way the entire Internet operates. Let's talk about how blockchain works and how it can be used to improve our AI.

Code for this video: https://github.com/llSourcell/The_Power_of_the_blockchain

Charles-David's winning code: https://github.com/alkaya/Optimizer-cotw

Parminder's runner up code: https://github.com/Trion129/Gradient-Descent-from-scratch

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More learning resources: https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984 https://medium.com/towards-data-science/the-blockchain-and-ai-fbfa691f10e0 https://medium.com/the-intrepid-review/how-does-the-blockchain-work-for-dummies-explained-simply-9f94d386e093 https://www.oreilly.com/ideas/understanding-the-blockchain https://github.com/golemfactory/golem

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We'll train an image classifier in Keras using a Tensorflow backend, then serve it to the browser using a super simple Flask backend. We can then deploy this flask app to google cloud using a few commands. Woot!

Code for this video: https://github.com/llSourcell/how_to_deploy_a_keras_model_to_production

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More learning resources: https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html#exporting-a-model-with-tensorflow-serving http://machinelearningmastery.com/deploy-machine-learning-model-to-production/ https://news.ycombinator.com/item?id=13821217 https://www.quora.com/Should-keras-be-used-to-deploy-deep-learning-models-in-production

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I ask my ex-roommate Brayden McLean 67 questions about life at Lyft as a Data Scientist. Lyft is a ride-sharing company very similar to Uber and is expanding globally. Brayden genuinely wants to help improve the state of the world and talks about how his work at Lyft helps him do that.

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More on Brayden: http://brayden.strikingly.com/

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Which optimizer should we use to train our neural network? Tensorflow gives us lots of options, and there are way too many acronyms. We'll go over how the most popular ones work and in the process see how gradient descent has evolved over the years.

Code from this video (with coding challenge): https://github.com/llSourcell/The_evolution_of_gradient_descent/

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More learning resources: http://sebastianruder.com/optimizing-gradient-descent/ https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer http://machinelearningmastery.com/gradient-descent-for-machine-learning/ http://cs231n.github.io/optimization-1/ https://www.cs.toronto.edu/~hinton/csc2515/notes/lec6tutorial.pdf https://www.youtube.com/watch?v=umAeJ7LMCfU

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Once we've trained a model, we need a way of deploying it to a server so we can use it as a web or mobile app! We're going to use the Tensorflow Serving library to help us run a model on a server that we can then make HTTP requests to for data. We'll have the user upload an image and it will return a classification for that image.

Code for this video: https://github.com/llSourcell/How-to-Deploy-a-Tensorflow-Model-in-Production

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More learning resources: https://www.tensorflow.org/deploy/tfserve https://medium.com/osldev-blog/tensorflow-serving-practical-introduction-9ce29ccd63f https://tensorflow.github.io/serving/serving_basic.html https://gist.github.com/avloss/01e43d208fbdb2c5b4f9b50e71617cc8 https://github.com/tensorflow/serving http://fdahms.com/2017/03/05/tensorflow-serving-jvm-client/ https://books.google.com/books?id=rsyqDQAAQBAJ&pg=PA159&lpg=PA159&dq=tensorflow+serving+tutorial&source=bl&ots=7NaS-3V6sr&sig=UmnIPMM5djXAwidPyneWD7P667w&hl=en&sa=X&ved=0ahUKEwiw-J6uq5nUAhUph1QKHX5ZBdoQ6AEImgEwFA#v=onepage&q=tensorflow%20serving%20tutorial&f=false

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All neural networks use activation functions, but the reasons behind using them are never clear! Let's discuss what activation functions are, when they should be used, and what the difference between them is.

Sample code from this video: https://github.com/llSourcell/Which-Activation-Function-Should-I-Use

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More Learning resources: http://www.kdnuggets.com/2016/08/role-activation-function-neural-network.html http://cs231n.github.io/neural-networks-1/ https://www.quora.com/What-is-the-role-of-the-activation-function-in-a-neural-network https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons https://en.wikibooks.org/wiki/Artificial_Neural_Networks/Activation_Functions https://stackoverflow.com/questions/9782071/why-must-a-nonlinear-activation-function-be-used-in-a-backpropagation-neural-net https://papers.nips.cc/paper/874-how-to-choose-an-activation-function.pdf http://neuralnetworksanddeeplearning.com/chap4.html https://medium.com/towards-data-science/activation-functions-in-neural-networks-58115cda9c96 https://medium.com/autonomous-agents/mathematical-foundation-for-activation-functions-in-artificial-neural-networks-a51c9dd7c089

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Let's use an evolutionary algorithm to improve a Tetris AI! We'll be coding this in Javascript (gasp) because I want to try something different. Through the process of selection, crossover, and mutation our AI will eventually be able to reach the high score of 500 in record time.

Code for this video: https://github.com/llSourcell/How_to_make_an_evolutionary_tetris_bot

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More Learning resources: https://www.youtube.com/watch?v=L--IxUH4fac https://luckytoilet.wordpress.com/2011/05/27/coding-a-tetris-ai-using-a-genetic-algorithm/ https://codemyroad.wordpress.com/2013/04/14/tetris-ai-the-near-perfect-player/ http://www.cs.uml.edu/ecg/uploads/AIfall10/eshahar_rwest_GATetris.pdf http://cs229.stanford.edu/proj2015/238_poster.pdf

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I traveled to Amsterdam for a week to speak at The Next Web Conference on AI Safety. While roaming the streets of the city, I decided to take some shots and formulate a video on the same topic for you guys. In the battle of good vs evil, it's up to our community to ensure good wins. I'll resume the coding videos next week when I get back to San Francisco.

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I'll post a link to the talk once it's up, here's an article in the mean time: https://thenextweb.com/artificial-intelligence/2017/05/18/how-to-keep-ai-from-killing-us-all/#.tnw_VaEi7vjZ

More Learning resources: https://futureoflife.org/ai-safety-research/ https://iamtrask.github.io/2017/03/17/safe-ai/ https://blog.openai.com/concrete-ai-safety-problems/ https://intelligence.org/why-ai-safety/ https://80000hours.org/career-reviews/artificial-intelligence-risk-research/ https://foundational-research.org/files/suffering-focused-ai-safety.pdf

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We're going to use Udacity's car simulator app as an environment to create our own autonomous agent! We'll use Keras to train a convolutional neural network on images from the car's cameras as well as steering angles from human driving. Using just those 2 data points, it'll be able to drive itself on any road.

Code for this video: https://github.com/llSourcell/How_to_simulate_a_self_driving_car

More Learning resources: https://hackernoon.com/five-skills-self-driving-companies-need-8546d2aba7c1 http://nicolovaligi.com/reading-list-udacity-self-driving-challenge-3.html https://www.linkedin.com/pulse/teaching-car-how-drive-using-deep-learning-muhieddine-el-kaissi http://selfdrivingcars.mit.edu/ http://blog.davidsingleton.org/nnrccar/ https://www.coursera.org/learn/machine-learning/lecture/zYS8T/autonomous-driving https://www.udacity.com/drive

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Let's discuss whether you should train your models locally or in the cloud. I'll go through several dedicated GPU options, then compare three cloud options; AWS, Google Cloud, and FloydHub. I was not endorsed by anyone for this.

Code for this video: https://github.com/floydhub/fast-style-transfer

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High Budget GPU: Titan XP https://www.amazon.com/NVIDIA-GeForce-Pascal-GDDR5X-900-1G611-2500-000/dp/B01JLKP3IS

Medium Budget GPU: https://www.amazon.com/MSI-GAMING-GTX-1060-6G/dp/B01IEKYD5U

Small Budget GPU: https://www.amazon.com/dp/B01MF7EQJZ

Build a Deep Learning machine: https://medium.com/@ncondo/build-a-deep-learning-rig-for-800-4434e21a424f https://medium.com/towards-data-science/building-your-own-deep-learning-box-47b918aea1eb https://www.oreilly.com/learning/build-a-super-fast-deep-learning-machine-for-under-1000

More learning resources: http://www.infoworld.com/article/3179785/cloud-computing/aws-vs-azure-vs-google-cloud-which-free-tier-is-best.html https://thehftguy.com/2016/06/15/gce-vs-aws-in-2016-why-you-should-never-use-amazon/ https://medium.com/@davidmytton/aws-vs-google-cloud-flexibility-vs-operational-simplicity-dca4324b03d4 https://news.ycombinator.com/item?id=13659914

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EVGA GeForce 04G-P4-6253-KR, GTX 1050 Ti SC GAMING, 4GB GDDR5, DX12 OSD Support (PXOC) Graphics Card

nVIDIA 12GB Titan X Pascal DVI HDMI 3x Displayport PCI Express 3.0 x16 Graphics Card 900-1G611-2500 (Renewed)

MSI GAMING GeForce GTX 1060 6GB GDRR5 192-bit HDCP Support DirectX 12 Dual TORX 2.0 Fan VR Ready Graphics Card (GTX 1060 GAMING X 6G)

The Differentiable Neural Computer is an awesome model that DeepMind recently released. It's a memory augmented network that can perform meta-learning (learning to learn). We'll go over it's architecture details and implement it ourselves in Tensorflow.

Code for this video: https://github.com/llSourcell/differentiable_neural_computer

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More learning resources: https://deepmind.com/blog/differentiable-neural-computers/ https://www.quora.com/How-groundbreaking-is-DeepMinds-Differentiable-neural-network https://github.com/dsindex/blog/wiki/%5Bdnc%5D-Differentiable-Neural-Computer https://blog.acolyer.org/2016/03/09/neural-turing-machines/ https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/

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I'll go through 5 methods that you can use to make money as a programmer! We are lucky in that our skill will only get more valuable to society over time. Links to everything I've discussed are below.

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Contract work: http://upwork.com/ http://freelancer.com/ https://github.com/engineerapart/TheRemoteFreelancer (huge list)

Improving your portfolio: http://www.codeofhonor.com/blog/marketing-yourself-as-a-programmer https://softwareengineering.stackexchange.com/questions/54506/how-to-market-yourself-as-a-software-developer/59875

Paul Graham's goldmine of essays on starting a startup: http://www.paulgraham.com/articles.html

Programming Challenges: http://topcoder.com/ https://www.hackerearth.com/ https://www.codechef.com/

Bounty listings: https://www.bountysource.com/ https://bountify.co/

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One-shot learning! In this last weekly video of the course, i'll explain how memory augmented neural networks can help achieve one-shot classification for a small labeled image dataset. We'll also go over the architecture of it's inspiration (the neural turing machine).

Code for this video (with challenge): https://github.com/llSourcell/How-to-Learn-from-Little-Data

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More learning resources: https://www.youtube.com/watch?v=CzQSQ_0Z-QU https://arxiv.org/abs/1605.06065 https://futuristech.info/posts/differential-neural-computer-from-deepmind-and-more-advances-in-backward-propagation https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/

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Generative Adversarial Nets are such a rich topic for exploration, we're going to build one that was released just 2 months ago called the "DiscoGAN" that lets us transfer the style between 2 datasets. And I'll be building this using Tensorflow.

Code for this video: https://github.com/llSourcell/GANS-for-style-transfer

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More learning resources: https://arxiv.org/abs/1703.05192 https://github.com/SKTBrain/DiscoGAN https://www.reddit.com/r/MachineLearning/comments/5zp0eu/r_170305192_learning_to_discover_crossdomain/ https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7

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I'll explain PyTorch's key features and compare it to the current most popular deep learning framework in the world (Tensorflow). We'll then write out a short PyTorch script to get a feel for the syntax. This library is becoming popular pretty fast, such is the nature of this incredible field.

Code for this video: https://github.com/llSourcell/pytorch_in_5_minutes

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More Learning Resources: https://www.youtube.com/watch?v=fJZew-fdNxw https://hackernoon.com/how-is-pytorch-different-from-tensorflow-2c90f44747d6 https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f https://fossbytes.com/pytorch-machine-learning-framework/ https://github.com/pytorch/examples http://pytorch.org/

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Generative Adversarial Networks are back! We'll use the cutting edge StackGAN architecture to let us generate images from text descriptions alone. This is pretty wild stuff and there is so much room for improvement. The possibilities are endless. I'll go through the architecture, code, and the implications of this technology for humanity.

Special shoutout to new Patrons Joshua Tobkin, Cameron Tofer, and Zarathustra Technologies. I'll add you guys to the credits next video.

Code for this video: https://github.com/llSourcell/how_to_convert_text_to_images

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More learning resources: https://www.youtube.com/watch?v=rAbhypxs1qQ https://www.youtube.com/watch?v=93yaf_kE0Fg https://arxiv.org/abs/1612.03242 http://cs.stanford.edu/people/karpathy/gan/ http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html

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We're going to build a GAN to generate some images using Tensorflow. This will help you grasp the architecture and intuition behind adversarial approaches to machine learning. We're building a Deep Convolutional GAN to generate MNIST digits.

Code for this video: https://github.com/llSourcell/Generative_Adversarial_networks_LIVE/blob/master/EZGAN.ipynb

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More Learning resources: http://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them http://www.cs.toronto.edu/~dtarlow/pos14/talks/goodfellow.pdf https://datawarrior.wordpress.com/2017/02/03/generative-adversarial-networks/ https://www.quora.com/What-are-Generative-Adversarial-Networks http://nuit-blanche.blogspot.com/2017/01/nips-2016-tutorial-generative.html http://www.paddlepaddle.org/develop/doc/tutorials/gan/index_en.html http://gkalliatakis.com/blog/delving-deep-into-gans

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Generative Adversarial Networks. It's time. We're going to use a Deep Convolutional GAN to generate images of the alien language from the movie arrival that we can then stitch together to animate into video. I'll go over the architecture of a GAN and then we'll implement one ourselves!

Code for this video (coding challenge included): https://github.com/llSourcell/how_to_generate_video

Nemanja's winning code: https://github.com/Nemzy/video_generator

Niyas' Runner up code: https://github.com/niazangels/vae-pokedex

and his blog post: https://hackernoon.com/how-to-autoencode-your-pok%C3%A9mon-6b0f5c7b7d97

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More Learning Resources: http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/ https://blog.openai.com/generative-models/ http://cs.stanford.edu/people/karpathy/gan/ https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Generative-Adversarial-Networks http://wiseodd.github.io/techblog/2016/09/17/gan-tensorflow/ https://www.slideshare.net/ThomasDaSilvaPaula/a-very-gentle-introduction-to-generative-adversarial-networks-aka-gans-71614428 http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39

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We'll build a Variational Autoencoder using Tensorflow to generate images. We'll go through several examples including digit images and pokemon images. VAE's allow us to generate, compress, denoise, and even fuse images together. They are an incredibly powerful tool and we'll go over the implementation details (math included) in this live session.

Code: https://github.com/llSourcell/how_to_generate_images_with_tensorflow_LIVE

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More Learning resources: https://arxiv.org/abs/1606.05908 https://github.com/stitchfix/fauxtograph http://deeplearning.jp/cvae/ https://ift6266h17.wordpress.com/2017/03/26/q3-reparameterization-trick-of-variational-autoencoder/ https://www.quora.com/What-is-the-latent-loss-in-variational-autoencoders https://www.slideshare.net/ShaiHarel/variational-autoencoder-talk

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We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :)

Code for this video: https://github.com/llSourcell/how_to_generate_images

Mike's Winning Code: https://github.com/xkortex/how_to_win_slot_machines/blob/master/WallStBandits.ipynb

SG's Runner up Code: https://github.com/esha-sg/Intro-DeepLearning-Siraj-Week13

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2 things -The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5. -The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that.

More Learning resources: https://jaan.io/what-is-variational-autoencoder-vae-tutorial/ http://kvfrans.com/variational-autoencoders-explained/ http://blog.fastforwardlabs.com/2016/08/12/introducing-variational-autoencoders-in-prose-and.html http://blog.fastforwardlabs.com/2016/08/22/under-the-hood-of-the-variational-autoencoder-in.html http://blog.evjang.com/2016/11/tutorial-categorical-variational.html https://jmetzen.github.io/2015-11-27/vae.html

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We're going to use the policy gradient technique from reinforcement learning to beat the game of Pong. We'll use OpenAI's Universe as an environment for our agent and I'll go over the process of setting it up as well as the math behind the PG method in detail.

Microphone popping issues end at 11:15 . That cannot happen again. Udacity is aware of this and will be more prepared next time.

Code for this video: https://github.com/llSourcell/Policy_Gradients_to_beat_Pong

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More Learning resources: http://www.scholarpedia.org/article/Policy_gradient_methods http://proceedings.mlr.press/v32/silver14.pdf http://karpathy.github.io/2016/05/31/rl/ http://home.deib.polimi.it/restelli/MyWebSite/pdf/rl7.pdf http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching_files/pg.pdf https://github.com/dennybritz/reinforcement-learning/tree/master/PolicyGradient

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Etan and I are going to show you how to make smart blinds using the Raspberry Pi and some components! The blinds will detect if a person is near it using facial detection and open. When a person moves away, it will close. We attached a servo motor to the blind to get it to do this automatically. I met Etan when I was attending Columbia in NYC and now he's a software engineer at Square. I've known him as a hardware hacker, so I thought it'd be fun to collab! We started off soldering in a board, but in the end we decided to use a breadboard instead (so no need to solder) This was a 2 day project.

Code and instructions for this project: https://github.com/llSourcell/hardware_demo

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Etan's Twitter: https://twitter.com/etanz?lang=en

More learning resources: https://www.raspberrypi.org/learning/getting-started-with-picamera/worksheet/ https://learn.adafruit.com/adafruits-raspberry-pi-lesson-8-using-a-servo-motor/hardware https://learn.adafruit.com/category/raspberry-pi http://docs.opencv.org/2.4.13/doc/tutorials/tutorials.html

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We'll learn how to solve the multi-armed bandit problem (maximizing success for a given slot machine) using a reinforcement learning technique called policy gradients.

Code for this video: https://github.com/llSourcell/how_to_win_slot_machines

Mike's winning code: https://github.com/xkortex/Siraj_Chatbot_Challenge

Vishal's runner up code: https://github.com/erilyth/DeepLearning-Challenges/tree/master/Text_Based_Chatbot

this coding challenge was really close, so i'm also going to put code for 3rd place just this time (Eibriel): https://github.com/Eibriel/ice-cream-truck

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More Learning resources: http://karpathy.github.io/2016/05/31/rl/ http://minpy.readthedocs.io/en/latest/tutorial/rl_policy_gradient_tutorial/rl_policy_gradient.html http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html http://kvfrans.com/simple-algoritms-for-solving-cartpole/ https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149 https://dataorigami.net/blogs/napkin-folding/79031811-multi-armed-bandits

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We're going to learn how the visualizer that comes with Tensorflow works in this live stream. We'll go through a bunch of different features and test out its functionality both programmatically and visually.

4:41 code begins 37:07 tensorboard visualization begins

Code for this video: https://github.com/llSourcell/how_to_use_tensorboard_live/tree/master

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More Learning resources: https://www.tensorflow.org/get_started/summaries_and_tensorboard http://ischlag.github.io/2016/06/04/how-to-use-tensorboard/ https://www.youtube.com/watch?v=3bownM3L5zM https://blog.altoros.com/visualizing-tensorflow-graphs-with-tensorboard.html http://www.titiapps.com/hands-on-tensorboard-tensorflow-dev-summit-2017/

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Streaming Live from UploadVR's Studio in San Francisco!: https://www.youtube.com/uploadvr Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Let's discuss the math behind back-propagation. We'll go over the 3 terms from Calculus you need to understand it (derivatives, partial derivatives, and the chain rule and implement it programmatically.

Code for this video: https://github.com/llSourcell/how_to_do_math_for_deep_learning

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I've used this code in a previous video. I had to keep the code as simple as possible in order to add on these mathematical explanations and keep it at around 5 minutes.

More Learning resources: https://mihaiv.wordpress.com/2010/02/08/backpropagation-algorithm/ http://outlace.com/Computational-Graph/ http://briandolhansky.com/blog/2013/9/27/artificial-neural-networks-backpropagation-part-4 https://jeremykun.com/2012/12/09/neural-networks-and-backpropagation/ https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/

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Forgot to add my patron shoutout at the end so special thanks to Patrons Tim Jiang, HG Oh, Hoang, Advait Shinde, Vijay Daniel & Umesh Rangasamy Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Lets Make a Question Answering chatbot using the bleeding edge in deep learning (Dynamic Memory Network). We'll go over different chatbot methodologies, then dive into how memory networks work, with accompanying code in Keras.

Code + Challenge for this video: https://github.com/llSourcell/How_to_make_a_chatbot

Nemanja's Winning Code: https://github.com/Nemzy/language-translation/blob/master/neural_machine_translation.ipynb

Vishal's Runner up code: https://github.com/erilyth/DeepLearning-Challenges/tree/master/Language_Translation

Web app to run the code yourself: https://ethancaballero.pythonanywhere.com

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More Learning resources: https://www.youtube.com/watch?v=FCtpHt6JEI8&t=643s https://www.youtube.com/watch?v=Qf0BqEk5n3o&t=637s https://yerevann.github.io/2016/02/05/implementing-dynamic-memory-networks/ https://www.youtube.com/watch?v=2A5DKPA5lAw http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/ https://github.com/domluna/memn2n

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The point of this is to demonstrate the concept of gradient descent. Gradient descent is the most popular optimization strategy in deep learning, in particular an implementation of it called backpropagation. We are using gradient descent as our optimization strategy for linear regression. We'll draw the line of best fit to measure the relationship between student test scores and the amount of hours studied.

Code for this video: https://github.com/llSourcell/linear_regression_live

Yes, I've done this video before. But I'm doing it again because 1. Gradient Descent is really important. Know how it works. 2. Last time was in Google Hangouts (Ghetto) this is better quality

More learning resources: https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/ https://en.wikipedia.org/wiki/Gradient_descent http://machinelearningmastery.com/gradient-descent-for-machine-learning/ https://www.analyticsvidhya.com/blog/2017/03/introduction-to-gradient-descent-algorithm-along-its-variants/ http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/

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Let's build a Sequence to Sequence model in Tensorflow to learn exactly how they work. You can use this model to make chatbots, language translators, text generators, and much more . We'll go over memory, attention, and some variants (like bidirectional layers) both programmatically and mathematically.

Code for this video: https://github.com/llSourcell/seq2seq_model_live/blob/master/2-seq2seq-advanced.ipynb

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More Learning resources: https://www.tensorflow.org/tutorials/seq2seq http://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html http://suriyadeepan.github.io/2016-06-28-easy-seq2seq/ https://indico.io/blog/sequence-modeling-neuralnets-part1/ http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

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Streaming Live from UploadVR's Studio in San Francisco!: https://www.youtube.com/uploadvr

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Let's build our own language translator using Tensorflow! We'll go over several translation methods and talk about how Google Translate is able to achieve state of the art performance.

Code for this video: https://github.com/llSourcell/How_to_make_a_language_translator

Ryan's Winning Code: https://github.com/rtlee9/recipe-summarization

Sarah's Runner-up Code: https://github.com/scollins83/teal_deer

More Learning Resources: https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa https://www.tensorflow.org/tutorials/seq2seq https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/ https://www.youtube.com/watch?v=vxibD6VaOfI http://neural-monkey.readthedocs.io/en/latest/machine_translation.html http://blog.systransoft.com/how-does-neural-machine-translation-work/ http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/ https://blog.altoros.com/enabling-multilingual-neural-machine-translation-with-tensorflow.html https://www.quora.com/How-can-I-build-a-machine-translation-system https://blog.heuritech.com/2016/01/20/attention-mechanism/ https://smerity.com/articles/2016/google_nmt_arch.html

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Credits to Biggi Hilmars for the intro tune Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/

We're going to build an LSTM network in Tensorflow (no Keras) to generate text after training on Wikipedia articles. You'll learn how an LSTM cell works programmatically since we'll build one using TF's math functions and how you can parse a similar dataset

Code: https://github.com/llSourcell/wiki_generator_live

Dataset: https://metamind.io/research/the-wikitext-long-term-dependency-language-modeling-dataset/

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Learning resources: http://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw http://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://github.com/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb https://chunml.github.io/ChunML.github.io/project/Creating-Text-Generator-Using-Recurrent-Neural-Network/ http://deeplearningathome.com/2016/10/Text-generation-using-deep-recurrent-neural-networks.html https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/ http://genekogan.com/works/learning-sequences/

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Streaming Live from UploadVR's Studio in San Francisco!: https://www.youtube.com/uploadvr Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory.

Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer

Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer

More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/

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This live session will focus on the details of music generation using the Tensorflow library. The goal is for you to understand the details of how to encode music, feed it to a well tuned model, and use it to generate really cool sounds. And I'm going to NOT use Google Hangouts, instead I'll do this with a green screen and a DSLR camera :)

Code for this video: https://github.com/llSourcell/music_demo_live/

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My Udacity course is open for enrollments until this Saturday at midnight: https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

More Learning Resources: http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/ http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/ https://github.com/hexahedria/biaxial-rnn-music-composition http://www.hexahedria.com/2016/08/08/summer-research-on-the-hmc-intelligent-music-software-team https://magenta.tensorflow.org/ https://github.com/farizrahman4u/seq2seq http://stackoverflow.com/questions/14448380/how-do-i-read-a-midi-file-change-its-instrument-and-write-it-back https://github.com/vishnubob/python-midi

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Streaming Live from UploadVR's Studio in San Francisco!: https://www.youtube.com/uploadvr Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/

I'll show you the 5 steps to succeed in any technical interview. We'll go over what a great study plan looks like, resources to help you find jobs, and how you should conduct yourself during the interview.

Please Subscribe! That is the one thing you could do that would make me happiest.

Links from the video below

My Code School (Intro to Data Structures): https://www.youtube.com/watch?v=92S4zgXN17o&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=1

MIT Open Courseware (Intro to Algorithms): https://www.youtube.com/watch?v=HtSuA80QTyo&index=1&list=PLSX2U_ZE4Huk19DPn34oZlygPbsig380X

HackerEarth and HackerRank: https://www.hackerearth.com/ https://www.hackerrank.com/

Programming Interview Exposed: http://books.lihui.org/cs2/Wiley%20-%20Programming%20Interviews%20Exposed_Secrets%20to%20Landing%20Your%20Next%20Job%20(2000).pdf

Cracking the Coding Interview: https://github.com/yuanhui-yang/Cracking-the-Coding-Interview/blob/master/Cracking%20the%20Coding%20Interview%20-%204th%20Edition.pdf

How to Conduct a Mock Interview: http://web.stanford.edu/dept/CTL/Oralcomm/Microsoft%20Word%20-%20How%20to%20Conduct%20Mock%20Interviews.pdf

Angellist: https://angel.co/

HackerNews Who's Hiring: https://news.ycombinator.com/item?id=13541679

Making a great resume: https://medium.com/@order_group/job-interview-and-good-resume-cv-tips-for-programmers-from-our-experts-3aa626c825ab#.ssdw5a2th

Passing the Interview: http://blog.triplebyte.com/how-to-pass-a-programming-interview

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We're going to build a music generating neural network trained on jazz songs in Keras. I'll go over the history of algorithmic generation, then we'll walk step by step through the process of how LSTM networks help us generate music.

Coding Challenge for this video: https://github.com/llSourcell/How-to-Generate-Music-Demo

Vishal's Winning Code: https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Art_Generation

Michael's Runner up code: https://github.com/michalpelka/How-to-Generate-Art-Demo/blob/master/demo.ipynb

More Learning Resources: https://medium.com/@shiyan/understanding-lstm-and-its-diagrams-37e2f46f1714#.swstv6z61 http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html https://magenta.tensorflow.org/2016/06/10/recurrent-neural-network-generation-tutorial/ http://deeplearning.net/tutorial/rnnrbm.html https://maraoz.com/2016/02/02/abc-rnn/ http://www.cs.cmu.edu/~music//cmsip/slides/05-algo-comp.pdf http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/ https://www.reddit.com/r/algorithmicmusic/

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Thanks Ji-Sung Kim for the example code: https://deepjazz.io Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

We're going to learn about all the details of style transfer (especially the math) using just Tensorflow. The goal of this session is for you to understand the details behind how style+content loss is calculated and minimized. We'll also talk about future discoveries.

Code for this video: https://github.com/llSourcell/How_to_do_style_transfer_in_tensorflow

Learning resources: http://www.makeuseof.com/tag/create-neural-paintings-deepstyle-ubuntu/ https://blog.paperspace.com/art-with-neural-networks/ https://www.tensorflow.org/versions/r0.11/how_tos/ https://no2147483647.wordpress.com/2015/12/21/deep-learning-for-hackers-with-mxnet-2/ https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/ http://kawahara.ca/deep-dreams-and-a-neural-algorithm-of-artistic-style-slides-and-explanations/ http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style

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We're going to learn how to use deep learning to convert an image into the style of an artist that we choose. We'll go over the history of computer generated art, then dive into the details of how this process works and why deep learning does it so well.

Coding challenge for this video: https://github.com/llSourcell/How-to-Generate-Art-Demo

Itai's winning code: https://github.com/etai83/lstm_stock_prediction

Andreas' runner up code: https://github.com/AndysDeepAbstractions/How-to-Predict-Stock-Prices-Easily-Demo/blob/master/stockdemo.ipynb

More learning resources: https://harishnarayanan.org/writing/artistic-style-transfer/ https://ml4a.github.io/ml4a/style_transfer/ http://genekogan.com/works/style-transfer/ https://arxiv.org/abs/1508.06576 https://jvns.ca/blog/2017/02/12/neural-style/

Style transfer apps: http://www.pikazoapp.com/ http://deepart.io/ https://artisto.my.com/ https://prisma-ai.com/

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Song at the beginning is called Everyday by Carly Comando jurassic park inception visualization is from http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/ Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

We're going to use Tensorflow to predict the next event in a time series dataset. This can be applied to any kind of sequential data.

Code for this video: https://github.com/llSourcell/rnn_tutorial

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More learning resources: https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://www.reddit.com/r/MachineLearning/comments/4ervmf/tensorflow_rnn_time_series_prediction/ https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/ http://nbviewer.jupyter.org/github/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb

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I ask 67 questions to Dandelion Mane as we walk around Google HQ in Mountain View, California. Dandelion used to be my roommate and is now working on the Tensorflow team at Google. Specifically, he works on the visualizer called Tensorboard. I ask him a lot of rapid fire questions, some machine learning related and some about his life in general.

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More info on Dandelion: https://www.linkedin.com/in/danmane

His recent talk on Tensorboard apart of the Tensorflow Dev Summit https://www.youtube.com/watch?v=eBbEDRsCmv4&t=165s

And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power.

Coding challenge for this video: https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo

Vishal's winning code: https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier

Jie's runner up code: https://github.com/jiexunsee/Simple-Inception-Transfer-Learning

More Learning Resources: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://deeplearning.net/tutorial/lstm.html https://deeplearning4j.org/lstm.html https://www.tensorflow.org/tutorials/recurrent http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/

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music in the intro is chambermaid swing by parov stelar Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

We're going to build an image classifier using just Tensorflow (no Keras). This will be in depth, the goal for this video is for you to fully understand how a Convolutional Neural Network works. We'll visualize the filters we create along the way as well.

Code for this video: https://github.com/llSourcell/How_to_make_a_tensorflow_image_classifier_LIVE/blob/master/demonotes.ipynb

More CNN learning resources: http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/ https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ http://cs231n.github.io/convolutional-networks/ http://deeplearning.net/tutorial/lenet.html http://neuralnetworksanddeeplearning.com/chap6.html http://machinelearningmastery.com/crash-course-convolutional-neural-networks/ https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/

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We're going to make our own Image Classifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.

Coding challenge for this video: https://github.com/llSourcell/how_to_make_an_image_classifier

Charles-David's winning code: https://github.com/alkaya/TFmyValentine-cotw

Dalai's runner-up code: https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating

More Learning Resources: http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/ https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/ http://cs231n.github.io/convolutional-networks/ http://deeplearning.net/tutorial/lenet.html https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ http://neuralnetworksanddeeplearning.com/chap6.html http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/ http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/ https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2

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We're going to compare some different techniques that reduce the dimensionality of our data so we can visualize it. We'll go through each one step by step including the math and I'll answer questions along the way. And I freestyle.

Code for this video: https://github.com/llSourcell/How_to_Simplify_Your_Data-LIVE-

Links from the video: https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ http://setosa.io/ev/eigenvectors-and-eigenvalues/

More learning resources: https://plot.ly/ipython-notebooks/principal-component-analysis/ http://sebastianraschka.com/Articles/2014_pca_step_by_step.html https://www.quora.com/What-is-the-difference-between-LDA-and-PCA-for-dimension-reduction https://www.quora.com/What-advantages-the-t-sne-algorithm-has-over-pca http://stats.stackexchange.com/questions/123040/whats-wrong-with-t-sne-vs-pca-for-dimensional-reduction-using-r https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm

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In this video, we'll go through data preprocessing steps for 3 different datasets. We'll also go in depth on a dimensionality reduction technique called Principal Component Analysis.

Coding challenge for this video: https://github.com/llSourcell/How_to_Make_Data_Amazing

Charles-David's Winning Code: https://github.com/alkaya/earthquake-cotw

Siby Jack Grove's Runner-up code: https://github.com/sibyjackgrove/Earthquake_predict/blob/master/earthquake_predict.ipynb

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More Learning Resources: http://www.cs.ccsu.edu/~markov/ccsu_courses/datamining-3.html http://www.slideshare.net/jasonrodrigues/data-preprocessing-5609305 http://iasri.res.in/ebook/win_school_aa/notes/Data_Preprocessing.pdf http://staffwww.itn.liu.se/~aidvi/courses/06/dm/lectures/lec2.pdf http://ufldl.stanford.edu/wiki/index.php/Data_Preprocessing http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://plot.ly/ipython-notebooks/principal-component-analysis/

Public datasets: https://github.com/caesar0301/awesome-public-datasets https://aws.amazon.com/public-datasets/ http://archive.ics.uci.edu/ml/index.html https://dreamtolearn.com/ryan/1001_datasets

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In this video, we're going to look at several different type of recommender systems in an iPython notebook. Popularity based, item-item collaborative, then user-item collaborative. Then we'll touch on the bleeding edge in deep learning at the end. Also I freestyle. Twice lol.

Code for this video: https://github.com/llSourcell/recommender_live

More learning resources: http://tech.hulu.com/blog/2016/08/01/cfnade.html https://blogs.msdn.microsoft.com/carlnol/2012/06/23/co-occurrence-approach-to-an-item-based-recommender/ https://www.mapr.com/blog/inside-look-at-components-of-recommendation-engine https://www.ics.uci.edu/~welling/teaching/CS77Bwinter12/presentations/course_Ricci/13-Item-to-Item-Matrix-CF.pdf https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/ http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/

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Let's learn about some key math concepts behind deep learning shall we? We'll build a 3 layer neural network and dive into some key concepts that makes deep learning give us such incredible results.

Coding challenge for this video: https://github.com/llSourcell/how_to_do_math_for_deep_learning

Jovian's Winning Code: https://github.com/jovianlin/siraj-intro-to-DL-03/blob/master/Siraj%2003%20Challenge.ipynb

Vishal's Runner up Code: https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Sentiment_Analysis

Linear Algebra cheatsheet: http://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf

Calculus cheatsheet: http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf

Statistics cheatsheet: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf

And if you have never had experience with any of these 3 and want to learn from absolute scratch, I'd recommend the respective KhanAcademy courses: https://www.khanacademy.org/math

More Learning Resources: https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf http://www.vision.jhu.edu/tutorials/ICCV15-Tutorial-Math-Deep-Learning-Intro-Rene-Joan.pdf http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/

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And Part I of this book is so dope, seriously: http://www.deeplearningbook.org/

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In this video, we'll use a Game of Thrones dataset to create word vectors. Then we'll map these word vectors out on a graph and use them to tell us related words that we input. We'll learn how to process a dataset from scratch, go over the word vectorization process, and visualization techniques all in one session.

Code for this video: https://github.com/llSourcell/word_vectors_game_of_thrones-LIVE

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More learning resources: https://www.tensorflow.org/tutorials/word2vec/ https://radimrehurek.com/gensim/models/word2vec.html https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words http://sebastianruder.com/word-embeddings-1/ http://natureofcode.com/book/chapter-1-vectors/

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In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python.

Coding Challenge for this video: https://github.com/llSourcell/How_to_do_Sentiment_Analysis

Ludo's winning code: https://github.com/ludobouan/pure-numpy-feedfowardNN

See Jie Xun's runner up code: https://github.com/jiexunsee/Neural-Network-with-Python

Tutorial on setting up an AMI using AWS: http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/

More learning resources: http://deeplearning.net/tutorial/lstm.html https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html https://www.kaggle.com/c/word2vec-nlp-tutorial

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If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part.

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In this live session I'll introduce & give an overview of Google's Deep Learning library, Tensorflow. Then we'll use it to build a neural network capable of predicting housing prices, with me explaining every step along the way.

Code for this video: https://github.com/llSourcell/How_to_use_Tensorflow_for_classification-LIVE

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More learning resources: https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/ https://www.oreilly.com/learning/hello-tensorflow https://www.tensorflow.org/tutorials/mnist/beginners/ https://github.com/aymericdamien/TensorFlow-Examples https://www.youtube.com/watch?v=2FmcHiLCwTU&t=84s https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf

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I ask 67 questions to the founder of Google X, self-driving car pioneer, former Director of the Stanford AI lab, and President of Udacity Sebastian Thrun. We take a stroll around Udacity HQ in Mountain View, California.

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More on Sebastian: https://en.wikipedia.org/wiki/Sebastian_Thrun

lol he answered 'the matrix' as his favorite movie too. the pattern is real guys.

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How do we learn? In this video, I'll discuss our brain's biological neural network, then we'll talk about how an artificial neural network works. We'll create our own single layer feedforward network in Python, demo it, and analyze the implications of our results. This is the 2nd weekly video in my intro to deep learning series (Udacity nanodegree)

The coding challenge for this video: https://github.com/llSourcell/Make_a_neural_network

Ludo's winning code: https://github.com/ludobouan/linear-regression-sklearn

Amanullah's runner up code: https://github.com/amanullahtariq/MLAlgorithm/tree/eca367287f7874e08a790ce0b0c21567e0b38a22/Challenge/LinearRegression

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More learning resources: https://www.mcb80x.org/ http://cogsci.stackexchange.com/questions/7880/what-is-the-difference-between-biological-and-artificial-neural-networks https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.fn92gnrar http://natureofcode.com/book/chapter-10-neural-networks/ https://blog.dbrgn.ch/2013/3/26/perceptrons-in-python/ http://neuralnetworksanddeeplearning.com/chap2.html https://iamtrask.github.io/2015/07/27/python-network-part2/

The guy at the beginning is my Jeet Kune Do instructor (Sifu Tim). Send him an email at sifutimr@gmail.com if you thought he was cool in the video. He would absolutely love it. Special thanks Catherine Olsson of OpenAI for being the hook to my backpropagation rap.

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I'll perform linear regression from scratch in Python using a method called 'Gradient Descent' to determine the relationship between student test scores & amount of hours studied. This will be about 50 lines of code and I'll deep dive into the math behind this.

Code for this video: https://github.com/llSourcell/linear_regression_live

Please subscribe! And like. And comment. That's what keeps me going. And yes, this video is apart of my 'Intro to Deep Learning series'

More learning resources: http://mathinsight.org/image/partial_derivative_as_slope http://www.dummies.com/education/math/calculus/how-to-use-a-partial-derivative-to-measure-a-slope-in-three-dimensions/ https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/ https://www.quora.com/What-is-an-intuitive-explanation-of-gradient-descent http://machinelearningmastery.com/gradient-descent-for-machine-learning/

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Welcome to Intro to Deep Learning! This course is for anyone who wants to become a deep learning engineer. I'll take you from the very basics of deep learning to the bleeding edge over the course of 4 months. In this video, we’ll predict an animal’s body weight given it’s brain weight using linear regression via 10 lines of Python. I’ll have a live session every Wednesday at 10 AM PST that covers my weekly video topics in depth. You can click on the little notification bell next to the subscribe button to get an email notification whenever I’m live. And each session is recorded and uploaded to this channel in case you miss it. This Youtube content is 100% created by me (from the writing to the editing, etc.) , it’ll all be released on my channel, and it’s totally free.

I am also very proud and excited to announce my new, exclusive partnership with Udacity. Together, we’re offering the new Deep Learning Nanodegree Foundation program. If you want to take your game to the next level, this is for you! Especially since Udacity will be providing guaranteed admission to their groundbreaking Artificial Intelligence and Self-Driving Car Nanodegree programs to all graduates. They’re offering discounted limited-time pricing, so enroll now to enjoy the unique projects, program sets, and expert reviews. Plus, their community is amazing, so don’t forget to join the Slack channel after you enroll (I’ll be in there too!) And hey, I’m getting paid a small royalty from each enrollment, so let’s do this together!

Link to the Udacity nanodegree: https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

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This weeks coding challenge (these weekly challenges are not related to the Udacity nanodegree projects, those are additional): https://github.com/llSourcell/linear_regression_demo

Mick’s winning code: https://github.com/mickvanhulst/q_learning

Vishal’s runner-up code: https://github.com/erilyth/Q-Learning-on-Mazes

More learning resources: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/ https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer https://onlinecourses.science.psu.edu/stat501/node/250 http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/

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The vast majority of this course will use Tensorflow. It's just this first example that's using scikit-learn.

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I'll be using OpenCV + Python to detect strawberries in an image. This will take about 45 minutes and it'll be less than 100 lines of code.

Code for this video is here: https://github.com/llSourcell/Object_Detection_demo_LIVE

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More learning resources: http://docs.opencv.org/2.4/doc/tutorials/tutorials.html https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html https://www.youtube.com/watch?v=lJYEup-0gJo https://realpython.com/blog/python/face-recognition-with-python/ https://gravityjack.com/news/opencv-python-3-homebrew/ http://www.simplecv.org/

Join us in the Wizards Slack channel: http://wizards.herokuapp.com

And please support me on Patreon!: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, I go over the history of reinforcement learning then talk about how a type of reinforcement learning called Q learning works. We'll then write a 10 line python script for a Q learning bot in a 5x5 grid that will help it go from point A to point B as fast as possible.

The coding challenge for this video is here: https://github.com/llSourcell/q_learning_demo

More Learning resources: http://hunch.net/~jl/projects/RL/RLTheoryTutorial.pdf http://www2.econ.iastate.edu/tesfatsi/RLUsersGuide.ICAC2005.pdf https://www.quora.com/What-are-some-good-tutorials-on-reinforcement-learning http://burlap.cs.brown.edu/tutorials/cpl/p3.html http://outlace.com/Reinforcement-Learning-Part-1/ http://firsttimeprogrammer.blogspot.com/2016/09/getting-ai-smarter-with-q-learning.html

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In the last live stream I said there would be a big reveal, that reveal is coming next Friday (1/13).

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I'm going to go through the steps necessary to install OpenAI's Universe, then we'll build our own game bot using reinforcement learning. This code will be in Python.

*Update - I said something big was coming out this Friday in this video. I just got out of a meeting with the PR team i'm working with for promotion and they pleaded with me to release it next Friday (1/13) instead. So that''ll happen then. Just wanted to let you guys know.

Code for this video: https://github.com/llSourcell/OpenAI_Game_Bot_Live_stream

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Some past submissions for the 'Make a Game Bot' Challenge using OpenAI's Universe': https://github.com/av80r/coaster_racer_coding_challenge (winner) https://github.com/rhnvrm/universe-coaster-racer-challenge

More Learning Resources: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0 https://www.youtube.com/watch?v=mGYU5t8MO7s http://karpathy.github.io/2016/05/31/rl/ http://www.wildml.com/category/reinforcement-learning/

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This is my end of the year video! I wanted to do something a little different. I traveled to Portland, Oregon for a week to explore/meet people and decided to use the shots I took while hiking to illustrate how math is all around us in the natural world. We can and will discover the rules of intelligence. The fact that it is governed by mathematics only makes it that much more beautiful. From simple rules emerge incredible complexity.

Vishal's Winning code: https://github.com/erilyth/visualize_dataset_demo

Sethu's runner up code: https://github.com/sethuiyer/visualize-GOT

Original peer-reviewed paper in Science mag by Cambridge Professor Stolum (cited 221 times) on how the average sinuosity of all rivers is pi: http://raaf.org/pdfs/meandering_river.pdf

More Learning Resources: https://www.comsol.com/multiphysics/navier-stokes-equations https://www.theguardian.com/science/2016/nov/21/magic-numbers-can-maths-equations-be-beautiful https://westhunt.wordpress.com/2013/06/07/the-breeders-equation/ https://www.youtube.com/watch?v=GzCvlFRISIM

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The background music is the Interstellar theme by Hans Zimmer Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we'll make a bot using a technique called 'Neuroevolution' to defeat the popular game of asteroids! This will be in Javascript. I talk about the architecture (neurons, layers, networks, genomes, generations) and the interesting actions we'll take to improve our bot (like breeding and mutation).

The code for this video is here: https://github.com/llSourcell/asteroids_game_bot_LIVE

More learning resources: http://eplex.cs.ucf.edu/hyperNEATpage/ http://stackoverflow.com/questions/31708478/how-to-evolve-weights-of-a-neural-network-in-neuroevolution http://nn.cs.utexas.edu/?neuroevolution

Shoutout to Daniel Shiffman! This is apart of a collaboration with him: https://www.youtube.com/watch?v=hacZU523FyM&t=94s

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In this video, we'll visualize a dataset of body metrics collected by giving people a fitness tracking device. We'll go over the steps necessary to preprocess the data, then use a technique called T-SNE to reduce the dimensionality of our data so we can visualize it.

Code + challenge for this video: https://github.com/llSourcell/visualize_dataset_demo

Keagan's winning code: https://github.com/WeldFire/prepare_dataset_challenge

Vishal's runner-up code: https://github.com/erilyth/Pokemon-Type-Classification-Challenge

Join us in the Wizards Slack channel: http://wizards.herokuapp.com/

Live T-SNE demo in the browser: http://cs.stanford.edu/people/karpathy/tsnejs/

More learning resources: https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm https://indico.io/blog/visualizing-with-t-sne/ http://blog.applied.ai/visualising-high-dimensional-data/ http://machinelearningmastery.com/visualize-machine-learning-data-python-pandas/

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I'll use pure Javascript to build a neural network that evolves via a genetic algorithm to eventually become amazing at Tic Tac Toe!

Code for this video is here: https://github.com/llSourcell/tic_tac_toe_neural_network-LIVE

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Some things i mentioned in the video ---

Free Harvard Neuroscience course: https://www.mcb80x.org/

Great newer Deep Learning Books: http://www.deeplearningbook.org/ http://www.mlyearning.org/

Also tweet Andrew Ng directly and ask him if he will interview with @sirajraval . I'm trying to get him on my 'Really Quick Questions' series.

Please subscribe, like, and comment! That's what keeps me going.

and please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

I ask 67 questions to OpenAI Engineer Catherine Olsson as we take a stroll around OpenAI HQ in San Francisco. Catherine graciously agreed to an interview right after the release of OpenAI's Universe. I ask her questions that range from her deepest fears to her favorite Operating System.

Please hit that subscribe button if you liked this interview!

And like. And comment. That's what keeps me going.

Join other Wizards on our Slack channel: http://wizards.herokuapp.com/

Check out OpenAI's new Universe release: https://github.com/openai/universe

Here's her Linkedin: https://www.linkedin.com/in/catherineolsson

Side note - I just changed my youtube username to my full name, since that's what I want to be known by.

And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating.

The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge

Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge

Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge

Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/

Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets

More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf

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We're going to create a visual grid of squares with obstacles in it. Then, we'll use computer vision and a path planning algorithm to find the optimal route from point A to point B in the grid. You'll need the OpenCV, scikit-image, and numpy libraries installed for python.

Please subscribe! and like and comment. That's what keeps me going.

The code for this video is here: https://github.com/llSourcell/path_planning_demo_live

Join us in our Slack channel: http://wizards.herokuapp.com/

More learning resources: https://www.youtube.com/watch?v=sAoBeujec74 https://www.raywenderlich.com/4946/introduction-to-a-pathfinding http://docs.opencv.org/2.4/doc/tutorials/tutorials.html https://www.udacity.com/course/introduction-to-computer-vision--ud810

and please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

I ask 67 questions to Google Engineer and AI researcher Joel Shor as we take a stroll through the Googleplex in Mountain View, California. Joel and I have been friends for a while and he gladly agreed to an interview. I ask him questions that range from his sleeping patterns to his deep learning library of choice (tensorflow)

Please hit that subscribe button if you liked this interview!

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Join other Wizards on our Slack channel: http://wizards.herokuapp.com/

Joel's team recently released a paper on image compression using recurrent neural nets: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45534.pdf https://research.googleblog.com/2016/09/image-compression-with-neural-networks.html

Here's his Linkedin: https://www.linkedin.com/in/joel-shor-20552260

And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we'll make a super simple speech recognizer in 20 lines of Python using the Tensorflow machine learning library. I go over the history of speech recognition research, then explain (and rap about) how we can build our own speech recognition system using the power of deep learning.

The code for this video is here: https://github.com/llSourcell/tensorflow_speech_recognition_demo

Mick's winning code: https://github.com/mickvanhulst/tf_chatbot_lotr

The weekly challenge can be found at the end of the 'Make a Game Bot' video: https://www.youtube.com/watch?v=mGYU5t8MO7s

More learning resources: https://www.superlectures.com/iscslp2014/tutorial-4-deep-learning-for-speech-generation-and-synthesis http://andrew.gibiansky.com/blog/machine-learning/speech-recognition-neural-networks/ https://www.youtube.com/watch?v=LFDU2GX4AqM https://www.youtube.com/watch?v=g-sndkf7mCs

Please subscribe! And like and comment. That's what keeps me going.

And please support me on Patreon! I don't work for anyone, although I did make a one-off video for OpenAI because I love them: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this live stream, we're going to use Tensorflow to build a convolutional neural network capable of classifying images. You'll need 'tensorflow' and the 'future' python libraries installed. The connection was laggy for the live stream and that won't happen again.

4:09-5:50 (The connection drops out)

The code for this video is here: https://github.com/llSourcell/tensorflow_neural_net_live_demo/blob/master/README.md

More learning resources: https://www.tensorflow.org/versions/r0.10/tutorials/mnist/beginners/index.html http://deeplearning.net/tutorial/gettingstarted.html http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/ https://www.oreilly.com/learning/not-another-mnist-tutorial-with-tensorflow?log-in Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we first go over the history of video game AI, then I introduce OpenAI's Universe, which lets you build a bot that can play thousands of different video games. It has environments for all sorts of games, from Space Invaders, to Grand Theft Auto, to Protein folding simulations.

CODING CHALLENGE DUE DATE: Thursday, December 15th. (which is 2 weeks, not 1 week from now like usual)

The coding challenge for this video is to make a bot that's better than this video's demo code. Post your Github link in the comments! For your README, just include a 1-3 sentence description of your strategy and instructions on how to run the code.The demo code can be found in the README of the Universe repo. : https://github.com/openai/universe

And a Tensorflow based starter bot can be found here: https://github.com/openai/universe-starter-agent

Some great learning resources: https://www.nervanasys.com/openai/ http://karpathy.github.io/2016/05/31/rl/ http://kvfrans.com/simple-algoritms-for-solving-cartpole/ https://kofzor.github.io/Reinforcement_Learning_101/

Join other Wizards on our Slack channel: https://wizards.herokuapp.com/

OpenAI asked me to make this video and I gladly said yes. They are awesome!!

Please subscribe! And like and comment. That's what keeps me going.

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We'll go over how chatbots have evolved over the years and how Deep Learning has made them way better. Then we'll build our own chatbot using the Tensorflow machine learning library in Python.

The code & coding challenge for this video are here: https://github.com/llSourcell/tensorflow_chatbot

Georgi's winning code for this week: https://github.com/petkofff/p_vs_np_challenge

Mick's Runner up code for this week: https://github.com/mickvanhulst/travSalesman

Join other Wizards on our Slack room: https://wizards.herokuapp.com

Live sequence to sequence chatbot demo: http://neuralconvo.huggingface.co/

Some more useful resources on chatbots: http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/ http://venturebeat.com/2016/08/01/how-deep-reinforcement-learning-can-help-chatbots/ http://web.stanford.edu/class/cs124/lec/chatbot.pdf

More resources on Tensorflow: http://lauragelston.ghost.io/speakeasy-pt2/ https://speakerdeck.com/inureyes/building-ai-chat-bot-using-python-3-and-tensorflow

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In this video, we're gonna make a neural network in Python from scratch!

Code for this video is here: https://github.com/llSourcell/make_a_neural_net_live_demo

Join other Wizards on our Slack: http://wizards.herokuapp.com/

More Neural Network resources: http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html https://www.quora.com/What-is-an-intuitive-explanation-for-neural-networks http://karpathy.github.io/neuralnets/

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In this video, I explain perhaps the most famous problem in all of Computer Science. Does P = NP? I define the terms and give examples of each. We also programmatically go through the traveling salesman problem. I experiment with a little bit of mixed reality in this video as well.

Code for this video: https://github.com/llSourcell/p_vs_np_challenge

Nichole's winning code: https://github.com/nhrigby

Mick's runner-up code: https://github.com/mickvanhulst

Join the Wizard's Slack Channel: https://wizards.herokuapp.com/

Some more great P vs NP resources: https://danielmiessler.com/study/pvsnp/ https://qntm.org/pnp http://news.mit.edu/2009/explainer-pnp https://blog.codinghorror.com/the-girl-who-proved-p-np/ https://medium.com/the-physics-arxiv-blog/the-astounding-link-between-the-p-np-problem-and-the-quantum-nature-of-universe-7ef5eea6fd7a

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And please support me on Patreon! https://www.patreon.com/user?u=3191693

I used the Tilt Brush mixed reality app to draw the complexity classes for fun. Thanks Az Balabanian and the Upload Collective for letting me shoot videos in VR! : https://www.Azadux.com/mixed-reality https://www.Uploadcollective.com Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we'll build a Python web scraper that retrieves the top 20 most frequent words with their percentages in an English Wikipedia article.

Code for this video is here: https://github.com/llSourcell/web_scraper_live_demo

Check out my friend Zoe Hong's Youtube channel for some cool fashion and illustration educational videos: https://www.youtube.com/channel/UCMQ_mPIBPi4IMpYEmuyOMqQ

Please subscribe, comment, and like! That's what keeps me going.

2 more web scraping tutorials that are pretty good:

http://web.stanford.edu/~zlotnick/TextAsData/Web_Scraping_with_Beautiful_Soup.html

https://blog.miguelgrinberg.com/post/easy-web-scraping-with-python

Let me know of what types of things you'd like me to code in the future for live sessions, always open to suggestions. And please support me on Patreon!

https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/

I'm Siraj and I teach people how to build cool things with code. I make videos that teach you how to create games, chatbots, self driving cars, programs that create art & music, neural networks, and way more. This song is a parody of Eminem's Not Afraid. Hope you guys like it!

Special thanks to these 3 Youtubers. They're featured at the end of my video:

How to ADHD: https://www.youtube.com/c/howtoadhd Photos in Color: https://www.youtube.com/user/photosincolor This Guy Edits: https://www.youtube.com/user/svenpape

Join the Wizards Slack Channel: https://wizards.herokuapp.com/

Please like, subscribe, and comment! That's what keeps me going.

More info on the P=NP problem: https://en.wikipedia.org/wiki/P_versus_NP_problem

Lyrics:

[Chorus] im not afraid to understand every model from end-to-end we’ll train this bot together to perform we’ll stack those layers uniform align our data pick a platform strap on if u really wanna learn to code

[Intro] Yeah, it’s been a ride I guess I had to, go to that place, to get to this one Now some of you, might still be in that place If you're trying to get out, just subscribe to me I'll get you there

[Verse] You can try and read these errors off my compiler before i make em, i dont lint my code so makes sense that I’d break em my algorithm is dope AF i didnt fake em when i code it i use Python i dont give a damn if it breaks, i’m doing this for you, lets hack it all chatty bots and games and machine made music, i keep it open source github username is source to all those who look down on me just call me sirajology our neural nets’ll see better than biology self driving cars will drive dreams to reality learnin syntax and formatting oh so properly until we get the BSOD on old technology learning from our data findin deltas and thetas we’re pushing every limit writing MATLAB (just kidding MATLAB sucks) i’ll git add i’ll git pull i’ll git push git commit until you wizards git from noob to git legit

Support me on Patreon here: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we build a Gamma Radiation Classifier and use Genetic Programming to pick the best Machine Learning model + hyper-parameters FOR US in 40 lines of Python.

Challenge for this video: https://github.com/llSourcell/genetic_algorithm_challenge

Peter's winning code: https://github.com/PeterMitrano/deep_dream_challenge

Kyle's Runner up code: https://github.com/ljlabs/deep_dream_challenge/blob/master/Dream_in_video.py

Great chapter on Genetic Algorithms: http://natureofcode.com/book/chapter-9-the-evolution-of-code/

Link to TPOT: https://github.com/rhiever/tpot

Join the Wizards Slack Channel: https://wizards.herokuapp.com/

Please like + subscribe + comment!

Please support me on Patreon!: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this session, I'm going to show you how to visualize data using the popular data visualization library (D3.js). This is useful for showing your results from machine learning algorithms, or just for you to understand what your data looks like. We visualize survivor stats from the popular Kaggle Titanic Survivor dataset.

The code in this video can be found here: https://github.com/llSourcell/D3_Data_visualization_live

Please support me on Patreon! I want to continue to do this Youtube channel full-time: https://www.patreon.com/user?u=3191693

Comment, Like, and Subscribe! That's what keeps me going. :) Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we replicate Google's Deep Dream code in 80 lines of Python using the Tensorflow machine learning library. Then we visualize it at the end.

The challenge for this video is here: https://github.com/llSourcell/deep_dream_challenge

Avhirup's winning stock prediction code: https://github.com/Avhirup/Stock-Market-Prediction-Challenge

Victor's runner-up code: https://github.com/ciurana2016/predict_stock_py

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

More Deep Dream tutorials:

http://www.alanzucconi.com/2016/05/25/generating-deep-dreams/ https://github.com/awanninger/deepdream http://ryankennedy.io/running-the-deep-dream/

Generate Deep Dream's online: http://deepdreamgenerator.com/generator-style

Still my favorite intro to neuroscience class: https://www.mcb80x.org/

Please subscribe! And share this video, like + comment. That's what keeps me going.

Please support me on Patreon if you like my videos: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video we're going to build the popular game Pong from scratch in Python, then train a neural network to become an unbeatable 2nd player! We use Tensorflow to build our neural net and pygame to build our Pong game.

The full, working code for this video is here: https://github.com/llSourcell/pong_neural_network_live

Unlike my previous 2 live sessions where i did less than 60 lines of code each, I tried to do about 400 lines of code in this one. So I didn't have time to get to everything!

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Please share this video, like, comment and subscribe! And please support me on Patreon!: https://www.patreon.com/user?u=3191693

That's what keeps me going. I love you all. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library.

The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices

Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py

Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/

Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/

This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft

Please share this video, like, comment and subscribe! That's what keeps me going.

and please support me on Patreon!: https://www.patreon.com/user?u=3191693

Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.

The coding challenge for this video is here:

https://github.com/llSourcell/recommender_system_challenge

The winner of last weeks coding challenge (Rohan Verma): https://twitter-sentiment-csv.herokuapp.com/ https://t.co/4eg8UdlaSB

The runner up (Arnaud Delauney): https://github.com/arnauddelaunay/twitter_sentiment_challenge

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

The LightFM Python Library: https://github.com/lyst/lightfm/tree/master/lightfm

Some great learning resources on recommender systems:

http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/

https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/

http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/

http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/

Best book to become a Python God: https://learnpythonthehardway.org/

Please share this video, like, comment and subscribe! That's what keeps me going.

Please support me on Patreon!: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is.

The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge

Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py

Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

More on TextBlob: https://textblob.readthedocs.io/en/dev/

Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work

Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis

Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html

Best book to become a Python god: https://learnpythonthehardway.org/

Please share this video, like, comment and subscribe! That's what keeps me going.

Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693

Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, I'll be building and training an LSTM Neural Network on a dataset of city names. Then it'll be able to generate new city names from scratch.

Code for this video: https://github.com/llSourcell/build_a_neural_net_live/blob/master/README.md

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Read up more on TFLearn: https://github.com/tflearn/tflearn

Incredible article on LSTMs: http://colah.github.io/posts/2015-08-Understanding-LSTMs/

If you liked this stream, support me on Patreon! I do this full-time currently. https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Welcome to the 1st Episode of Learn Python for Data Science! This series will teach you Python and Data Science at the same time! In this video we install Python and our text editor (Sublime Text), then build a gender classifier using the sci-kit learn library in just about 10 lines of code.

Please subscribe & share this video if you liked it!

The code for this video is here: https://github.com/llSourcell/gender_classification_challenge

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Download Python here: https://www.python.org/downloads/

Download Sublime Text here: https://www.sublimetext.com/3

Some Great simple sci-kit learn examples here: https://github.com/chribsen/simple-machine-learning-examples

and the official scikit website: http://scikit-learn.org/

Highly recommend this online book as supplementary reading material: https://learnpythonthehardway.org/book/

Wondering when to use which model? This chart helps, but keep in mind deep neural nets outperform pretty much any model given enough data and computing power. so use these when you don't have access to loads of data and compute: http://scikit-learn.org/stable/tutorial/machine_learning_map/

Thank you guys for watching! Subscribe, like, and comment! That's what keeps me going. Feel free to support me on Patreon:

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In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.

The code for the TensorFlow vs Theano part of the video is here: https://github.com/llSourcell/tensorflow_vs_theano

An article that explains the differences in more detail: https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Learn more about TF Learn here: https://github.com/tflearn/tflearn

and here: https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html

Learn more about TensorFlow here: https://www.oreilly.com/learning/hello-tensorflow

More on Keras here: http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

More on SciKit Learn here: http://scikit-learn.org/stable/tutorial/

More on Caffe here: http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html

More on Theano here: https://github.com/Newmu/Theano-Tutorials

Thanks for watching guys, I do this for you. If you like my videos, feel free to support me on Patreon and please LIKE, SUBSCRIBE, COMMENT, AND SHARE!

https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This is my first live stream ever. I'm going to be using OpenAI's Gym library to build a bot that gets better and better at playing a 2D game like Pac-Man.I'll also just be live to answer any questions about me, my life, and whatever else you guys wanna talk about!

Code for this video: https://github.com/llSourcell/build_a_game_bot_live

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

OpenAI Gym: https://github.com/openai/gym

An article with in-depth explanations: http://kvfrans.com/simple-algoritms-for-solving-cartpole/

Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, I go over some of the state of the art advances in music generation coming out of DeepMind. Then we build our own music generation script in Python using Tensorflow and a type of neural network called a Restricted Boltzmann Machine. Congrats to Rohan Verma (Winner) and Chih-Cheng Liang (runner-up) for their classifiers for scientists. The challenge for this video is to generate a happy/upbeat song using the RBM Script.

The code for this video is here: https://github.com/llSourcell/Music_Generator_Demo

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

The WaveNet blogpost with audio samples: https://deepmind.com/blog/wavenet-generative-model-raw-audio/

More on RBMs: http://deeplearning4j.org/restrictedboltzmannmachine.html

Another write up on music generation with Neural Networks: http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/

Interesting Machine Music Generation Project by Google: https://magenta.tensorflow.org/welcome-to-magenta

TensorFlow course on Udacity: https://www.udacity.com/course/deep-learning--ud730

Rohan's Classifier (Winner): https://github.com/rhnvrm/galaxy-image-classifier-tensorflow

Chih-Cheng's Classifier (Runner-up): https://github.com/ChihChengLiang/tensorflow-night-heron-classifier

Please subscribe, like, and comment! You guys are the reason I do this. Thanks so much for watching my videos! If you enjoy my videos, I'd appreciate your support on Patreon :)

https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/

In this video, we first go through the code for a simple handwritten character classifier in Python, then visualize it in Tensorboard. The point of this video was to showcase Tensorboard as a data visualization tool. We also use a more complex handwritten character classifier to further showcase all of Tensorboard's features. This was the hardest video I've ever had to make in terms of timing. It was really difficult to fit this many TB features into this time frame.

The code for this video is here:

https://github.com/llSourcell/Tensorboard_demo

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Challenge:

The challenge for this video is to visualize some audio data in Tensorboard. To date, I haven't seen any repos on GitHub that do this. The audio feature seems relatively new in Tensorboard. The first person who does this and posts it in the comments by Sept 30 2016 gets a shoutout from me in my video release on that date!

Tensorboard tutorial:

https://www.tensorflow.org/versions/r0.7/how_tos/summaries_and_tensorboard/index.html

Another good TB tutorial:

https://www.tensorflow.org/versions/r0.7/how_tos/graph_viz/index.html

An unofficial tutorial:

http://www.slideshare.net/hunkim/tensor-board

A video i found on tensorboard:

https://www.youtube.com/watch?v=zp5EtBvwQbw

I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon:

https://www.patreon.com/user?u=3191693

Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :) Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this episode we're going to train our own image classifier to detect Darth Vader images.

The code for this repository is here: https://github.com/llSourcell/tensorflow_image_classifier

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

The Challenge: The challenge for this episode is to create your own Image Classifier that would be a useful tool for scientists. Just post a clone of this repo that includes your retrained Inception Model (label it output_graph.pb). If it's too big for GitHub, just upload it to DropBox and post the link in your GitHub README. I'm going to judge all of them and the winner gets a shoutout from me in a future video, as well as a signed copy of my book 'Decentralized Applications'.

This CodeLab by Google is super useful in learning this stuff:

https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#0

This Tutorial by Google is also very useful:

https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html

This is a good informational video:

https://www.youtube.com/watch?v=VpDonQAKtE4

Really deep dive video on CNNs:

https://www.youtube.com/watch?v=FmpDIaiMIeA

I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon:

https://www.patreon.com/user?u=3191693

Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :)

edit: Credit to Clarifai for the first conv net diagram in the video Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

We're going to build our own version of Conway's famous Game of Life in 60 lines of Python! The Game of Life simulates biological evolution and is an example of how mind-boggling complexity can emerge from just a few simple rules. This video isn't straight up machine learning, but instead an intro into this way of thinking. If the universe works this way, our brains must as well. I truly believe that the master algorithm behind human intelligence will be much simpler than we expect.

Code for this video: https://github.com/llSourcell/simulation_demo

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Online Implementation of the Game of Life here: http://www.bitstorm.org/gameoflife/

Community dedicated to the Game of Life: http://conwaylife.com/

Awesome article on the Game of Life: http://www.math.cornell.edu/~lipa/mec/lesson6.html

Variations of the Game of Life: https://cs.stanford.edu/people/eroberts/courses/soco/projects/2008-09/modeling-natural-systems/gameOfLife2.html

Really, really good read-up on Cellular Automata: http://natureofcode.com/book/chapter-7-cellular-automata/

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, I explain the basics of recurrent neural networks. Then we code our own RNN in 80 lines of python (plus white-space) that predicts the sum of two binary numbers after training.

Code for this video:

https://github.com/llSourcell/recurrent_neural_net_demo

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Thank @iamtrask for a great RNN article: https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

and this piece by Karpathy on RNN's deserves some sort of award: http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Another great RNN article: http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/

Tensorflow RNNs: https://www.tensorflow.org/versions/r0.10/tutorials/recurrent/index.html

Thanks so much for watching my videos, I do this stuff for you guys. I'm about to hit 10K subscribers soon. If and when I do, I'm going to start working on a machine learning music video to celebrate! With better production quality than my last two!

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video is all about building a handwritten digit image classifier in Python in under 40 lines of code (not including spaces and comments). We'll use the popular library TensorFlow to do this.

Please subscribe! That would make me the happiest, and encourage me to output similar content.

The source code for this video is here: https://github.com/llSourcell/tensorflow_demo

Here are some great links on TensorFlow:

Tensorflow setup: https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#pip-installation

A similar written tutorial by Google: https://www.tensorflow.org/versions/r0.9/tutorials/mnist/beginners/index.html

Tensorflow Course: https://www.udacity.com/course/deep-learning--ud730

Awesome intro to Tensorflow: https://www.oreilly.com/learning/hello-tensorflow

Some other great introductory examples using Tensorflow: https://github.com/aymericdamien/TensorFlow-Examples

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we talk about how machine learning is used to create antivirus programs! Specifically, a classifier can be trained to detect whether or not some piece of software is malicious.

Check out my friend Danooct1's Youtube channel on viruses (dope AF):

https://www.youtube.com/user/danooct1

The code in the video is here:

https://github.com/llSourcell/antivirus_demo

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Paper 1: A Machine Learning Approach to Anomaly based detection on Android

https://arxiv.org/pdf/1512.04122.pdf

Paper 2: SMARTBot - A Behavior Detection Framework for Botnets

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792466/

Paper 3: A New Malware Detection Approach Using Bayesian Classification

https://arxiv.org/pdf/1608.00848v1.pdf

More on Machine Learning + Cybersecurity:

http://www.lancaster.ac.uk/pg/richarc2/dissertation.pdf

https://www.sec.in.tum.de/malware-detection-ws0910/

https://insights.sei.cmu.edu/sei_blog/2011/09/using-machine-learning-to-detect-malware-similarity.html

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:

https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Let's build a self driving car! In this video, I talk about how self driving cars work, then dive into 2 fresh papers that add modern improvements to autonomous vehicles. The self driving car that we build is in a simulated environment and is built using PyGame and the Keras machine learning library.

The code in the video is here:

https://github.com/llSourcell/Self-Driving-Car-Demo/

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Paper 1: Long term Planning for Short Term Prediction

http://arxiv.org/pdf/1602.01580v1.pdf

Paper 2: End-to-End Learning for Self-Driving Cars

https://arxiv.org/pdf/1604.07316v1.pdf

More on Reinforcement Learning:

http://www2.hawaii.edu/~chenx/ics699rl/grid/rl.html

https://www.quora.com/Artificial-Intelligence-What-is-an-intuitive-explanation-of-how-deep-Q-networks-DQN-work

http://www2.econ.iastate.edu/tesfatsi/RLUsersGuide.ICAC2005.pdf

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:

https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video is all about autoencoders! I start off explaining what an autoencoder is and how it works. Then I talk about some use cases for autoencoders and the special types of autoencoders we use for each of them. Finally, I programmatically go through an example of a simple autoencoder, followed by a demo.

The code for this video is here:

https://github.com/llSourcell/autoencoder_demo/tree/master

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Autoencoder live demo in the browser:

https://cs.stanford.edu/people/karpathy/convnetjs/demo/autoencoder.html

and here are some great links to read up on autoencoders:

http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/

http://lazyprogrammer.me/a-tutorial-on-autoencoders/

https://blog.keras.io/building-autoencoders-in-keras.html

https://www.quora.com/What-are-the-best-resources-for-learning-about-autoencoders-from-scratch

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:

https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This episode of Fresh Machine Learning is about generating rap lyrics! Lyrical generation is possible using either Hidden Markov Models or deep learning. In this episode, I go through a few past examples of what's been done before, then dive into our own example that we can code in Python. Welcome to the machine MC revolution!

The demo code for this video can be found here:

https://github.com/llSourcell/Rap_Lyric_Generator

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Try it out live here:

http://deepbeat.org/

I introduce three papers in this video

Unsupervised Rhyme Scheme Identification in Hip Hop Lyrics Using Hidden Markov Models:

http://link.springer.com/chapter/10.1007%2F978-3-642-39593-2_3

Modeling Hip Hop Challenge-Response Lyrics as Machine Translation:

http://www.illc.uva.nl/LaCo/CLS/papers/wu_hiphop_itg.pdf

DopeLearning: A Computational Approach to Rap Lyrics Generation:

http://arxiv.org/abs/1505.04771

More info about Hidden Markov Models:

https://www.youtube.com/watch?v=TPRoLreU9lA

https://www.quora.com/What-is-a-simple-explanation-of-the-Hidden-Markov-Model-algorithm

http://www.developerstation.org/2011/11/hidden-markov-models-for-dummies.html

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This episode of Fresh Machine Learning is all Tone Analysis. Tone analysis consists of not just analyzing sentiment (positive or negative), but also analyzing emotions as well as writing style. There are a lot of dimensions to tone, and in this episode I talk about what I consider to be 3 seminal papers in this field. At the end of the episode, we use IBM’s Watson Tone Analyzer API to build our own tone analysis web app.

The demo code for this video can be found here:

https://github.com/llSourcell/Tone-Analyzer

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

I introduce three papers in this video

Convolutional neural networks for sentence classification:

http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf

Text categorization using LSTM for region embeddings:

http://arxiv.org/pdf/1602.02373v2.pdf

Hierarchical attention networks for document classification:

https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf

More info about the IBM Watson Tone Analyzer API:

http://www.ibm.com/watson/developercloud/tone-analyzer.html

Some great notes, slides, and practice problems for NLP:

http://cs224d.stanford.edu/syllabus.html

Live demo of the Watson Tone Analyzer:

https://tone-analyzer-demo.mybluemix.net/

Really great long-form page talking about text classification

http://www.nltk.org/book/ch06.html

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This episode of Fresh Machine Learning is all about a relatively new concept called a Generative Adversarial Network. A model continuously tries to fool another model, until it can do so with ease. At that point, it can generate novel, authentic looking data! Very exciting stuff.

The demo code for this video is a set of adversarial Gaussian Distribution Curves in Python using Theano and PyPlot:

https://github.com/llSourcell/Generative-Adversarial-Network-Demo

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

I introduce two papers in this video

Generative Adversarial Networks:

https://arxiv.org/pdf/1406.2661v1.pdf

and the associated code:

https://github.com/goodfeli/adversarial

Generative Adversarial Text-to-Image Synthesis:

https://arxiv.org/pdf/1605.05396v2.pdf

and it's associated code is here:

https://github.com/reedscot/icml2016

Another really cool repo using GANs:

https://github.com/Newmu/dcgan_code

Great explanation of GANs:

http://soumith.ch/eyescream/

Live demo of a GAN:

http://cs.stanford.edu/people/karpathy/gan/

One more really great description of generative models:

https://openai.com/blog/generative-models/

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Welcome to Fresh Machine Learning! This is my new course dedicated to making bleeding edge machine learning accessible to developers everywhere.

The demo code for this video is a handwritten character classifier in Python using a One-Shot Learning technique with SciPy:

https://github.com/llSourcell/One-Shot-Learning-Demo

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

I introduce two papers in this video

Human Level Concept Learning through Probabilistic Program Induction:

http://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf

and it's associated code is in MatLab:

https://github.com/brendenlake/BPL

but Matlab requires $ to download and Python is better suited for building production apps. I found a great alternative though, these guys are really close to finishing this python library. It's called 'PyBPL' they are working on making the results from the paper generalized so that you can apply BPL to any kind of example dataset. I talked with the lead dev and he said they'd have working demos out in 3-6 weeks so follow this repo!!:

https://github.com/MaxwellRebo/PyBPL

One-Shot Learning with Memory Augmented Neural Networks:

https://arxiv.org/pdf/1605.06065v1.pdf

and it's associated code is here:

https://github.com/tristandeleu/ntm-one-shot

Also, here's another very recent One Shot Learning Paper from DeepMind that I couldn't squeeze into this video but is very interesting:

https://arxiv.org/pdf/1606.04080v1.pdf

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first API-based chatbot able to converse with a user around a topic of your choosing!

The code for this video is here: https://github.com/llSourcell/API_Chatbot

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

API.AI (My favorite Chatbot API): https://api.ai/

Nuance Mix: https://developer.nuance.com/mix

Wit.Ai: https://wit.ai/home

SiriKit: https://developer.apple.com/sirikit/

Chatbots are all about recognizing intent i.e what is the user saying to me? I thought this paper was really fascinating. It's called "Toward Computational Recognition of Humorous Intent": https://www.researchgate.net/profile/Julia_Taylor2/publication/228353850_Toward_computational_recognition_of_humorous_intent/links/0deec53b55d7fd4782000000.pdf

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first AI Writer able to write a short story based on an image that you input.

The code for this video is here: https://github.com/llSourcell/AI_Writer

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Great write-up on recurrent neural nets (LSTMs and GRUs) http://deeplearning4j.org/lstm.html

Paper on skip thought vectors: http://arxiv.org/pdf/1506.06726v1

Paper on Unifying Visual Semantic Embeddings: https://arxiv.org/pdf/1411.2539v1.pdf

You can test this code out at this site! It's really cool, they have a bunch of deep learning models in the cloud, you just have to upload an input and it gives you an output: http://www.somatic.io/models/2n6g7RZQ

If you're interested in NLP, check out Michael Collins course. This guy is such a G (it's free and open source!): https://www.coursera.org/course/nlangp

And check out this guy's free deep learning course on Udacity: https://www.udacity.com/course/deep-learning--ud730

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first AI Reader using Google's newly released pre-trained text parser, Parsey McParseface.

The code for this video is here: https://github.com/llSourcell/AI_Reader

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Here's the original blog post about Parsey: https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html

This is Google's repo for Parsey: https://github.com/tensorflow/models/tree/master/syntaxnet

If you're interested in NLP, check out Michael Collins course. This guy is such a G (he co-authored Parsey), I took this class at Columbia and it was one of the few where I actually attended every session. (it's free and open source!): https://www.coursera.org/course/nlangp

Link to API.AI in case you want to go that route: https://api.ai/

The political debate fact checker was an idea I had but never got around to building. It takes the transcript from a political debate, extracts the intent of a claim, queries it against google, perhaps scrapes some search result data and then assigns it a truthfulness rating out of 100. If it falls below a certain threshold, that person must be lying! How cool would that be?

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first Chatbot using the deep learning library Torch!

The code for this video is here: https://github.com/llSourcell/Chatbot-AI

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Here's the Neural Conversational Model paper (check out the machine-generated support conversations, they're mind-blowingly good): http://arxiv.org/pdf/1506.05869v3.pdf

You should train this baby in the cloud using AWS. See ML for Hackers #4 for a tutorial on how to use AWS: https://www.youtube.com/watch?v=eKmIVU8EUbw

Some great info on LSTM architecture: http://deeplearning4j.org/lstm.html

Link to Facebook's Chatbot API if you're curious: https://developers.facebook.com/blog/post/2016/04/12/bots-for-messenger/

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first AI Artist using the deep learning library Keras!

The code for this video is here: https://github.com/llSourcell/AI_Artist

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Here's the initial Google DeepDream blog post: http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html

A Deepdream web app: https://dreamscopeapp.com/

The Neural Style Paper: http://arxiv.org/pdf/1508.06576v2.pdf

Some great info on convolutional neural networks: http://colah.github.io/posts/2014-07-Conv-Nets-Modular/

You should train this baby in the cloud using AWS. See ML for Hackers #4 for a tutorial on how to use AWS: https://www.youtube.com/watch?v=eKmIVU8EUbw

This person went ahead and made a web app so you don't even have to compile the code to try this out: https://deepart.io/

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first movie recommender system in just 10 lines of C++. We train a neural network on a MovieLens dataset of movie ratings by different users to generate a top 10 recommendation list for the default user ID.

The code for this video is here (everything included): https://github.com/llSourcell/Movie_Recommender

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

The Original Amazon DSSTNE code is here: https://github.com/amznlabs/amazon-dsstne

Link to AWS: https://aws.amazon.com/

Link to FileZilla: https://sourceforge.net/projects/filezilla/

Paper I found pretty cool (a deep learning based rec system): https://arxiv.org/pdf/1409.2944.pdf

And a correction -- Scott grand recently tested it vs Tensorflow and reported not just a 2x, but a 15x speedup: https://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f#.loze1hltg

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first game AI in just 10 lines of Python. The AI can theoretically learn to master any game you train it on, but has only been tested on 2D Atari games so far.

The code for this video is here: https://github.com/llSourcell/Game-AI

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Tensorflow install instructions here: https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

Gym install instructions here: https://gym.openai.com/docs

Great course on the brain (I really love this course): https://www.mcb80x.org/

Original Deep Q Learner Paper: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

Lots of info on convolutional neural networks: http://cs231n.github.io/convolutional-networks/

Lots of info on reinforcement learning: http://www.nervanasys.com/demystifying-deep-reinforcement-learning/

I'm a fan of www.fomoro.com for cloud GPU computing since they are the only free-to-try cloud GPU provider I could find. Let me know if you find another!

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This video will get you up and running with your first AI composer in just 10 lines of Python. The app can compose british folk songs after training on an existing folk dataset.

The code for this video is here: https://github.com/llSourcell/AI_Composer

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

This is 'a' way to generate music, it's not necessarily the absolute best way. Another attempt I really like is this one since it can generate not just monophonic music, but polyphonic music as well:

http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/

Tensorflow install instructions here: https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

In a future video, I'll discuss how to easily use cloud GPU computing. Likely using www.fomoro.com

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

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This video will get you up and running with your first ML app in just 7 lines of Python. The app will be able to recognize Iris flowers.

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Follow the install instructions for TensorFlow here: https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

Follow the install instructions for SciKit Learn here: http://scikit-learn.org/stable/install.html

And here is a link to the repo for Skflow (the scikit interface for TensorFlow): https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn

My code sample is in the README of that repo under "Linear Classifier".

Map of Machine Learning Models: http://www.wangbo.info/img/mlmindmap.png

Map to pick the right model from SciKit Learn (although this doesn't take into account deep neural nets [just think -- lots of data? Just go with the DNN]): http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png

This is the first in my new application-focused machine learning series. The goal is to avoid anything math-heavy and focus on building things with machine learning libraries.

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Much more to come so please subscribe, like, and comment. That stuff is what encourages me to continue! Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

This is the 24 hour Bayes Impact Machine Learning Hackathon condensed into less than 5 minutes. I went to this hackathon kind of spontaneously just to see what it'd be like. Tobias, Stedman, and I were ready to take it on alone, but luckily we were joined by newcomers Fei and Matt. We made a data-driven job recommendation app for people who work in a declining industry. We didn't win but had a lot of fun and learned a lot. 10/10 would do it again.

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Hackathon details: http://bayeshack.org/

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Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words

Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture

Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis

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How does a Neural network work? Its the basis of deep learning and the reason why image recognition, chatbots, self driving cars, and language translation work! In this video, i'll use python to code up a neural network in just 4 minutes using just the numpy library, capable of doing matrix mathematics.

Code for this video: https://github.com/llSourcell/Make_a_neural_network

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Please Subscribe! That is the thing you could do that would make me happiest.

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2 Great Neural Net Tutorials:

(please subscribe for more videos like these! )

https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.l51z38s7f
https://iamtrask.github.io/2015/07/12/basic-python-network/

Awesome Tutorial Series on Neural Networks:

http://lumiverse.io/series/neural-networks-demystified

The Canonical Machine Learning Course:

https://www.coursera.org/learn/machine-learning

Curious just how inspired neural networks are from brain architecture? Take some time to learn about the human brain! This is my favorite intro to neuroscience course:

https://www.mcb80x.org/ Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Please subscribe!

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Link to the AlphaGo Paper: https://vk.com/doc-44016343_437229031?dl=56ce06e325d42fbc72

Learn More about Neural Networks: http://lumiverse.io/series/neural-networks-demystified

DeepMind's Mission, explained by it's co-founder: https://www.youtube.com/watch?v=rbsqaJwpu6A

The Canonical Machine Learning Course: https://www.coursera.org/learn/machine-learning/home/info

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Available on SoundCloud: http://bit.ly/1pi0su2 Click here to share this on Twitter: http://bit.ly/1nHKhVU Click here to post this on Facebook: http://on.fb.me/1QQ70ti

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Comment! Like! Subscribe!

=== Lyrics ===

Charlie Chaplin We have developed speed but we have shut ourselves in. machinery that gives abundance, has left us in want. our knowledge has made us cynical, our cleverness hard and unkind. We think too much and feel too little. More than machinery, we need humanity. More than cleverness, we need kindness and gentleness. Without these qualities life will be violent and all will be lost.

VERSE 1 i was never the type of kid to follow the rules my parents got so mad they said "study for school!" i don't give a fuck about tests and shit, don't try to grade me, don't tell me to go study and shit, i have my own dreams own life own mind online bitch try to take me offline i aint got time tryna to make my own way in this world trying make it, give liberation a whirl so lonely its just me myself one and only real friends is hard so many phonys its all good I'm moving up i know das wasup life is a marathon ill run till i throw up. Raised in houston but moved up to NYC to join that Ivy University damn right i was dazed and confused i refused to lose i had nothin to choose

But then i found a lab for robots, huh, damn that shit had my name on it.

Charlie Chaplin Do not despair. The misery that is now upon us is but the passing of greed. the bitterness of men who fear the way of human progress. the hate of men will pass, and dictators die, and the power they took from the people will return to the people. and so long as men die, liberty will never perish.

VERSE 2 I was up till the crack of dawn tryin find all the bots in my mind tryin to read shit online then i walked straight up to professor A "yo can i work in ur lab?' "sure, and we hope you stay" those words were sweet to my ears. signed up started playin with motors n gears no fear i was lead engineer, revolution was here "so visionary!" chuckle i made a robot startup tryin to make a robot so hot make some toast and a cold pot clean ya dish a whole lot but didn't work we failed a whole lot uh no money no sales i lost it i failed. passion was lost, taken by jack frost, no longer the boss, robotics was lost...i packed my bags. I left for the West.

Maybe I could find purpose again i didn't fuckin know

Charle Chaplin You the people have the power! The power to create machines, the power to create happiness. You the people have the power to make this life free and beautiful. To make this life a wonderful adventure. Then in the name of democracy, let us use that power. Let us all unite!

VERSE 3 Shoutout to SF my new home city fuckin love this place man its so very pretty, I'm a dichotomy sirajology i guess i learned robots were still hot to me. i be machine learnin neural nets Google no Alphabet all that shit uh, world is in flames we gotta make shit better make some robots do some real shit that matters. im tired of all the pain in the world all the hate all the lies all the war all the spies, tryin make shit blow up for miles, tryin to make us wild, back to the river nile, we gotta put brains in the robots make em so smart so hot fix shit a whole lot then we free to do a whole lot yea lets end all them fuckin sweatshops

but ay i think we can do it. don't u?

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Check out BitTorrent: http://www.bittorrent.com/

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Would love any feedback/opinions/questions in the comments section. Please subscribe if you haven't yet, thanks guys!

EDIT: I now have a professional grade microphone guys. Every video after this one will have awesome audio quality.

-Siraj

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

La'Zooz: http://www.lazooz.net/ OpenBazaar: https://openbazaar.org/ Synereo: http://www.synereo.com

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

My Book on DApps: http://www.amazon.com/Decentralized-Applications-Harnessing-Blockchain-Technology/dp/1491924543

Awesome Paper on DApps: https://github.com/DavidJohnstonCEO/DecentralizedApplications

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Decentralized Applications: Harnessing Bitcoin's Blockchain Technology

Ethereum smart contract tutorials: https://ethereum.gitbooks.io/frontier-guide/content/writing_contract.html

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Smart contract wiki: https://en.bitcoin.it/wiki/Contract

Voting app description: https://medium.com/@DomSchiener/publicvotes-ethereum-based-voting-application-3b691488b926#.3qm3zrdz1 Voting app repo: https://github.com/domschiener/publicvotes

Smart property startup: http://airlock.me/

Decentralized Uber: http://www.lazooz.net/

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Comment! Like! Subscribe!

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693

Twitter: https://twitter.com/sirajology Facebook: https://www.facebook.com/sirajology/ Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Link to Embark Dapp Framework: https://github.com/iurimatias/embark-framework

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

Link to Ethereum: https://ethereum.org/

Link to IPFS: https://ipfs.io/

Link to My Decentralized Apps Book: http://www.amazon.com/Decentralized-Applications-Harnessing-Blockchain-Technology/dp/1491924543

Link to Decentralized Apps Paper: https://github.com/DavidJohnstonCEO/DecentralizedApplications

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Decentralized Applications: Harnessing Bitcoin's Blockchain Technology

IPFS website: https://ipfs.io Decentralized apps video: https://www.youtube.com/watch?v=utmnexPw1bY

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

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Cool paper on Decentralized Apps: https://github.com/DavidJohnstonCEO/DecentralizedApplications My book on Decentralized Apps: http://www.amazon.com/Decentralized-Applications-Harnessing-Blockchain-Technology/dp/1491924543 IPFS: http://www.ipfs.io Ethereum: https://www.ethereum.org/

I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/

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Decentralized Applications: Harnessing Bitcoin's Blockchain Technology

Available on SoundCloud: http://bit.ly/1Jktv9C Click here to share this on Twitter: http://bit.ly/1mpqlY8 Click here to post this on Facebook: http://on.fb.me/1R2OLDg

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=== Lyrics ===

INTRO

Shoutout to all them bitcoin warriors out there Keep fighting the good fight. Don’t be afraid of the feds. Actually a healthy bit of caution is good but. Yeah. anyway

VERSE 1

Check my temperature, damn i got a fever it’s spelled BTC yeah i’m a believer the revolution started back in 2008 Satoshi put pen to paper said it aint too late

the future is now but money exchange is so behind lets make currency and cryptography align money and cryptography a beautiful match send a mil in a minute straight to Kenya and back

no middle men, no limits, no chargebacks make them digital stacks no wasting time on greenbacks corruption is enemy, all power centrally caging us we aint free oh so so many fearfully

network is owned by many not just the few if you wanna join open a wallet we’ll include you that’s right i’m the bitcoin baron just follow me i’ll take you to bitcoin heaven

HOOK

Freedom of currency Frees us from tyranny I said I couldn’t be without you BTC

Freedom of currency Frees us from tyranny I said I couldn’t be without you BTC

VERSE 2

i rock blockchains playin all them stock games it’s a public record with no central effort, uh open source protocol is straight up facts miners carry SHA 256 on they backs

they be generatin twenty one million coins make it rain bitcoin everyday free to join banking millions of peeps from china to brazil everyone wants a piece of that digital shill

They say absolute power corrupts absolutely well bitcoin disrupts that very same power rudely i’m looking at you politicians who lie you be running but aint nowhere to hide

It’s a network that proves its fair and just We don’t need a God ‘in encryption we trust’! I have bitcoin dreams of a world that is free a fantasy of BTC creativity

HOOK

Freedom of currency Frees us from tyranny I said I couldn’t be without you BTC

Freedom of currency Frees us from tyranny I said I couldn’t be without you BTC

VERSE 3

if you a bitcoin warrior stand up we the chosen ones who never give up we’ll grow this beast from the west to the east feds’ll try to stop us we can’t be policed

it started with one and now its millions but just wait pretty soon its billions yea. thats right.worldwide recognize. you can’t stop us. decentralized.

uh. public ledger. No secrets. Ever. heh. Think about it.

===Shoutout===

Corey Brier the awesome cameraman!

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Example of a USD pegged cryptocurrency: https://nubits.com/ Create your own cryptocurrency using Colored Coins: https://www.coinprism.com/ Stellar: https://www.stellar.org/ GridCoin: http://www.gridcoin.us/ ZeroCoin: http://zerocoin.org/ LiteCoin: https://litecoin.org/

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Comment! Like! Subscribe!

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Buy your first Bitcoin here: http://www.coinbase.com Bitcoin source code: https://github.com/bitcoin/bitcoin Cheap Bitcoin Miner: https://21.co/learn/ Expensive Bitcoin Miner: http://www.butterflylabs.com/ Good tutorials on building your first BTC apps: https://21.co/learn Great free online class for learning more about BTC: https://www.youtube.com/watch?v=fOMVZXLjKYo

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/