Little Book of Generative AI Foundations: a 195-page math primer on generative models
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Tianhua Chen has published a revised arXiv preprint, the Little Book of Generative AI Foundations, a 195-page derivation-focused introduction to the mathematics of generative AI. The book covers PCA, probabilistic PCA, variational autoencoders, diffusion models, normalising flows, GANs, Wasserstein GANs, and energy-based models. Version 2 adds visual explanations. The report appeared on Hacker News, where it reached 150+ points.
Written by AI from the articles below · updated Nov 3, 7:34 PM ET
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- Hacker News · AI (150+ points)BlogLittle Book of Generative AI Foundations offers a mathematical primer on generative models
AIA revised arXiv preprint by Tianhua Chen, a 195-page book, gives a derivation-focused introduction to generative AI's mathematical foundations. It connects PCA, probabilistic PCA, variational autoencoders, diffusion models, normalising flows, GANs, Wasserstein GANs, and energy-based models, and adds visual explanations in version 2.
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