Learning Deep Architectures for AI By Yoshua Bengio
http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf
https://deeplearning4j.org/restrictedboltzmannmachine
https://stats385.github.io/readings
Neural Network Design 2nd Edtion
http://hagan.okstate.edu/NNDesign.pdf#page=469
Visualizing and Understanding Convolutional Networks
https://arxiv.org/pdf/1311.2901v3.pdf
Why does deep and cheap learning work so well?∗
https://arxiv.org/pdf/1608.08225.pdf
Harmonic Analysis of Neural Networks
https://statweb.stanford.edu/~candes/papers/Harm_Net.pdf
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction Thomas Wiatowski and Helmut Bo ̈lcskei Dept. IT & EE, ETH Zurich, Switzerland September 2, 2016
https://arxiv.org/pdf/1512.06293.pdf
https://distill.pub/2016/handwriting/
https://www.quora.com/How-far-along-are-we-in-the-understanding-of-why-deep-learning-works
https://medium.com/intuitionmachine/the-holographic-principle-and-deep-learning-52c2d6da8d9
Two good papers on the subject: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization (NIPS'2014) andThe loss surface of multilayer networks (AISTATS'2015).
http://uschmajew.ins.uni-bonn.de/research/pub/uschmajew/bsu15preprint_rev.pdf
Why does deep and cheap learning work so well?∗
https://arxiv.org/pdf/1608.08225.pdf
https://www.quora.com/Why-does-deep-learning-work-so-well-in-the-real-world
http://motls.blogspot.com/2015/03/quantum-gravity-from-quantum-error.html
https://arxiv.org/pdf/1407.6552v2.pdf Advances on Tensor Network Theory: Symmetries, Fermions, Entanglement, and Holography
http://uschmajew.ins.uni-bonn.de/research/pub/uschmajew/bsu15preprint_rev.pdf
https://perimeterinstitute.ca/conferences/quantum-machine-learning
https://arxiv.org/abs/1704.01552v1