Resources

Books available online, related to the topics discussed. Continuously updated.

• Deep Learning - Foundations and Concepts, Christopher M. Bishop, Hugh Bishop (pdf)
• Pattern Classification and Machine Learning, Christopher M. Bishop (pdf)
• Model-Based Machine Learning, John Winn et al. (pdf)
• Mathematics for Machine Learning, Deisenroth, Aldo Faisal, Cheng S. Ong (pdf)
• Neuronal Dynamics, Wulfram Gerstner et al. (online)
• The Little Book of Deep Learning, Francois Fleuret (pdf)
• Schaum's Outline of Linear Algebra (6th Ed.), Seymour Lipschutz and Marc Lipson (pdf)
• Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David (pdf)
• Information Theory, Inference, and Learning Algorithms, David MacKay (pdf)
• An Introduction to Optimization (4th editions), Edwin KP Chong, Stanislaw H Zak (pdf)

Material for the course Statistics for data Science at EPFL:

• lecture slides: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
• lecture videos: 1 2 3 4a 4b 4c 5a 5b 5c 6a 6b 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
• exercises: 1 2 3 4 5 6 7 8 9 10 11 12
• solutions: 1 2 3 4 5 6 7 8 9 10 11 12
• probabilistic density, distribution and parameters for continuous and discrete distributions

Other useful resources:

• Jax ML blog: how to scale your model
• The Ultra-Scale Playbook: Training LLMs on GPU Clusters (pdf)
• Latex Mathematical Symbols
• The Matrix Cookbook
• Computing Gradients with Backpropagation (Automatic Differentiation), from the Univ Princeton course COS-324, by Ryan P. Adams (pdf)
• OpenAI's guide to Prompt Engineering