Books

• Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT press, 2016.
• Information Theory, Pattern Recognition and Neural Networks - David McKay, Cambridge University Press, 2003.
• Reinforcement Learning, an Introduction - Richard S. Sutton, Andrew G. Barto, MIT press, 2015.
• Neural Networks and Deep Learning - Michael A. Nielsen¹, 2015.
• An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, Springer, 2017.
• The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer, 2009.
• Bayesian Reasoning and Machine Learning - David Barber, Cambridge, 2012.
• Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, MIT Press, 2018.
• Python Data Science Handbook - Jake VanderPlas, O’Reilly, 2016.
• Applied Optimum Signal Processing - Sophocles J. Orfanidis, 2018.

Physics

• From Classical to Quantum ShannonTheory - Mark M. Wilde, Cambridge University Press, 2019.
• Quantum Country - A. Matuschak, M. Nielsen, 2020.

Articles

These are papers that I liked. I think they are good examples of well-written scientific papers.

• Computational Complexity for Physicists - Stephan Mertens, 2002.
• The Discipline of Machine Learning - Tom M. Mitchell, 2006.
• The Unreasonable Effectiveness of Data - Alon Halevy, Peter Norvig, and Fernando Pereira, 2009.
• Automatic Differentiation in Machine Learning: A Survey - Baydin et al., 2018.
• Stochastic Gradient Descent Tricks - Léon Bottou, 2012.
• Memory Networks - Weston et al., 2016.
• Understanding Dropout - Pierre Baldi, Peter Sadowski, 2013.
• Auto-Encoding Variational Bayes - Diederik P. Kingma, Max Welling, 2014.
• Wasserstein Auto-Encoders - Ilya Tolstikhin & al., 2018.
• Monte Carlo Gradient Estimation in Machine Learning - Shakir Mohamed & al., 2019.