The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
It offers researchers, students, and practitioners a structured, accessible reference for understanding the core mathematical derivations and relationships across modern generative AI architectures.
Tianhua Chen has published a 195-page preprint book titled "The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer" on arXiv. The primer provides a derivation-oriented introduction connecting the mathematics of major generative model families, including variational autoencoders, diffusion models, normalizing flows, autoregressive models, GANs, and energy-based models.