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
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