Revised preprint of generative AI mathematics foundations book released
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Tianhua Chen has released a revised preprint of "The Little Book of Generative AI Foundations," totaling 195 pages, with newly added visual explanations throughout the book. The book is derivation-oriented, connecting PCA, probabilistic PCA, VAEs, diffusion models, normalizing flows, autoregressive decomposition, GANs, Wasserstein GANs, and energy models, aimed at researchers, practitioners, and students to help them build a mathematical foundation for generative modeling.
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- Hacker News AIThe Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
This revised 195-page preprint offers a compact, derivation-oriented introduction to the mathematical foundations of generative AI, tracing a coherent path from PCA and probabilistic PCA through variational autoencoders, diffusion models, normalising flows, autoregressive factorisations, GANs, Wasserstein GANs, and energy-based models. It aims to make the structure of generative modelling more accessible while retaining the mathematics needed to understand how these models are derived and related, and is intended for mathematically curious researchers, practitioners, and students. The version adds visual explanations throughout and welcomes comments and corrections.
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