Resources

1. Deep Learning for Vision Systems, by Mohamed Elgendy
2. Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurelien Geron

1. Statistical Learning Theory, by Bruce Hajek and Maxim Raginsky, Lecture Notes for ECE 543, University of Illinois, Urbana-Champaign, IL, USA, 2021.
2. Deep Learning Theory, by Matus J. Telgarsky, Lecture Notes for CS 540, Version: 2021-10-27 v0.0-e7150f2d (alpha), University of Illinois, Urbana-Champaign, IL, USA, 2021.
3. Dive into Deep Learning, by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola.
4. Deep Learning, by Ian Goodfellow, Yoshua Bengio, Aaron Courville.
5. Deep Learning: Foundations and Concepts, by Christopher M. Bishop and Hugh Bishop.

1. Pro Git, by Scott Chacon and Ben Straub
2. Linus Torvalds' tech talk at Google on Git
3. Tools for Computer Scientists by Jarus and Wisely
4. Python 3.9.6 Official Documentation
5. PyTorch Documentation
6. TensorFlow Documentation

T1: Fundamentals of Learning

1. E.D. Sontag. ``VC Dimension of Neural Networks," in Neural Networks and Machine Learning, pp. 69-95, 1998.
2. M. Minsky and S. Papert, "Perceptrons: An Introduction to Computational Geometry," 2nd edition with corrections (first edition 1969), The MIT Press, Cambridge MA, ISBN 0-262-63022-2, 1972.
3. K. Hornik, M. Stinchcombe, H. White, "Multilayer Feedforward Networks are Universal Approximators," Neural Networks, vol. 2, Pergamon Press, pp. 359–366, 1989.
4. G. Cybenko, "Approximation by Superpositions of a Sigmoidal Function," Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303–314, 1989.
5. K. Hornik, "Approximation Capabilities of Multilayer Feedforward Networks". Neural Networks, vol. 4, no. 2, pp. 251–257, 1991.
6. Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, November 1998