Resources

Books

Deep Learning (Ian Goodfellow, Yoshua Bengio and Aaron Courville)
The Elements of Statistical Learning (Trevor Hastie, Robert Tibshirani, and Jerome Friedman)
Machine Learning: A Probabilistic Perspective (Kevin Murphy)
Artificial Intelligence: A Modern Approach (Stuart Russell and Peter Norvig)
Pattern Classification (Richard O. Duda, Peter E. Hart, and David G. Stork)
Convex Optimization (Stephen Boyd and Lieven Vandenberghe)
Networks, Crowds, and Markets (David Easley and Jon Kleinberg)
Neural Networks: A Comprehensive Foundation (Simon Haykin)
Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems (Chin-Teng Lin and George Lee)
Principles of Neural Science (Eric Kandel, James H. Schwartz, and Thomas Jessell)
Numerical Recipes: The Art of Scientific Computing (William Press et al.)

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