Automatic Differentiation

Theory

https://timvieira.github.io/blog/post/2017/08/18/backprop-is-not-just-the-chain-rule/

https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf

https://math.mit.edu/~stevenj/18.336/adjoint.pdf

https://colah.github.io/posts/2015-08-Backprop/

http://www.cs.cornell.edu/courses/cs5740/2017sp/lectures/04-nn-compgraph.pdf

Implementation

http://videolectures.net/deeplearning2017_johnson_automatic_differentiation/

https://github.com/mattjj/autodidact

Applications

https://arxiv.org/abs/1502.05767

Generative Models

https://openai.com/blog/generative-models/

https://deepmind.com/blog/wavenet-generative-model-raw-audio/

https://deepmind.com/blog/high-fidelity-speech-synthesis-wavenet/

https://deepgenerativemodels.github.io/notes/index.html

Probabilistic Graphical Models

https://ermongroup.github.io/cs228-notes/

Differentiable Programming

https://www.edge.org/response-detail/26794

https://medium.com/@karpathy/software-2-0-a64152b37c35

https://www.youtube.com/watch?v=LjWzgTPFu14

Neural ODE

https://github.com/rtqichen/torchdiffeq

https://news.ycombinator.com/item?id=18676986

Deep Learning with PyTorch

https://fleuret.org/ee559/