Modern and durable foundations for machine learning, deep learning, probability, and the math underneath contemporary AI systems.
Aurélien Géron · 2017
A practical modern baseline for moving from Python to real ML and deep-learning workflows.
Simon J D Prince
A newer deep-learning text that covers modern topics such as transformers and diffusion while staying readable.
Kevin P. Murphy · 2022
A rigorous but current probabilistic view of ML, useful for readers who need more than cookbook fluency.
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong · 2020
A compact math foundation covering linear algebra, calculus, optimization, probability, and statistics for ML.
François Chollet · 2017
A Keras 3-era deep learning text for readers who want practical neural-network work tied to modern generative AI workflows.
Sebastian Raschka · 2024
Also belongs here because implementing a small LLM is one of the clearest ways to understand modern model internals.