MTH 4320/5320 Neural Networks

Fall 2020

 

GitHub repository (notes, code, other information): https://github.com/rtwhite1546/Fall-2020-Neural-Networks

 

Below are some recommended references I use in the course. They are all freely available online.

 

Recommended Books

Michael Nielsen. Neural Networks and Deep Learning. Determination Press, 2015.

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. Springer-Verlag, 2009. 

 

Videos

Grand Sanderson (3Blue1Brown). Deep Learning YouTube series (feedforward nets).

Andrew Ng. Machine Learning (Stanford course videos).

Websites

Fei-Fei Li and others. CS231 Convolutional Neural Networks for Visual Recognition (notes from a Stanford course)

Course: http://cs231n.stanford.edu/

GitHub: https://cs231n.github.io/

Chris Olah. colah's blog (great visualizations for neural nets).

Distill (great visualizations for neural nets).

 

Sebastian Ruder. An overview of gradient descent optimization algorithms.

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