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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