Gradient descent, how neural networks learn | Deep learning, chapter 2

Stanford

To learn more, I highly recommend the book by Michael Nielsen
http://neuralnetworksanddeeplearning….
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/neural-ne…

MNIST database:
http://yann.lecun.com/exdb/mnist/

Also check out Chris Olah’s blog:
http://colah.github.io/
His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.

And if you like that, you’ll *love* the publications at distill:
https://distill.pub/

For more videos, Welch Labs also has some great series on machine learning:

“But I’ve already voraciously consumed Nielsen’s, Olah’s and Welch’s works”, I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book “Deep Learning” by Goodfellow, Bengio, and Courville.

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