ML FOUNDATIONS
New Mathematical Primer Explores the Foundations of Generative AI
Authored as a derivation-oriented guide, 'The Little Book of Generative AI Foundations' connects core models from VAEs and diffusion architectures to GANs.
A newly revised publication titled 'The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer' provides a compact, derivation-focused introduction to the mathematical core of modern generative artificial intelligence.
Rather than cataloging every implementation detail, the primer establishes a coherent trajectory connecting major model families, including PCA, probabilistic PCA, variational autoencoders, diffusion models, normalizing flows, autoregressive factorizations, GANs, and energy-based models. The text aims to make foundational mechanics accessible without diluting the mathematical rigor.