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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