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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