CEE 696-003: Deep Learning in CEE and ES

References

Goodfellow, Bengio and Courville, Deep Learning, MIT Press, 2016 (free access)

Francois Chollet, Deep Learning with Python 2nd Ed., Manning, 2021 (free access)

Charniak, Introduction to Deep Learning, MIT Press, 2019

Stone, Artificial Intelligence Engines: A tutorial introduction to the mathematics of deep learning, Sebtel Press, 2019

Strang, Linear Algebra and Learning from Data, Wellesley Cambridge Press, 2019

Michael Nielsen, Neural Networks and Deep Learning, Determination Press, 2015 (free access)

Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Springer, 2009 (free PDF)

Hal Daumé III, A Course in Machine Learning (free access)

Langr and Bok, GANs In Action, Manning Publication, 2019 (free access)

Brunton and Kutz, Data-Driven Science and Engineering - Machine Learning, Dynamical Systems and Control, Cambridge University Press, 2019 (lecture videos)

Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021 (draft pdf)

Hamilton, Graph Representation Learning Book, Morgan & Claypool, 2020 (pre-publication pdf)

Deep Learning Tutorial by Andrew NG group

Calin, Deep Learning Architectures: a mathematical approach, Springer, 2020

Arora et al., Theory of Deep Learning [draft]

Reviews

Higham and Higham, Deep Learning: An Introduction for Applied Mathematicians, SIAM Review, 2019

LeCun, Bengio and Hinton, Deep Learning, Nature, 521:436-444, 2015

Schmidhuber, Deep learning in Neural Networks: An overview, Neural networks, 61:85-117, 2015

State-of-the-Art Papers

Paper With Code

Etc

Linear Algebra

Petersen and Pedersen, The Matrix Cookbook

Trefethen and Bau, Numerical Linear Algebra, SIAM, 1997

Statistics

Jaynes, Probability Theory: The logic of science, Cambridge University Press, 2003 & Aubrey Clayton's Lectures on Jaynes’ book

Gelman, Carlin, Stern, Dunson, Vehtari and Rubin, Bayesian Data Anaysis, CRC Press, 2014 (pdf available for non-commerical purposes)