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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