MTH 4320/5320 Neural Networks
Fall 2020
GitHub repository (notes, code, other information): https://github.com/rtwhite1546/Fall-2020-Neural-Networks
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Fall 2021
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GitHub repository (notes, code, other information): https://github.com/rtwhite1546/Fall-2021-Neural-Networks
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References
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Below are some recommended references I use in the course. They are all freely available online.
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Recommended Books​
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Michael Nielsen. Neural Networks and Deep Learning. Determination Press, 2015.
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
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Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. Springer-Verlag, 2009.
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Videos
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Grand Sanderson (3Blue1Brown). Deep Learning YouTube series (feedforward nets).
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Andrew Ng. Machine Learning (Stanford course videos).
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Websites
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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/
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Chris Olah. colah's blog (great visualizations for neural nets).
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Distill (great visualizations for neural nets).
Sebastian Ruder. An overview of gradient descent optimization algorithms.
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