The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
Summary
The arXiv paper The Little Book of Generative AI Foundations presents a compact, derivation-oriented introduction to the mathematical underpinnings of generative AI. It traces core ideas across PCA, variational autoencoders, diffusion models, normalising flows, autoregressive models, GANs, and related frameworks, with an emphasis on building a cohesive foundation for researchers and practitioners. The revised version adds visual explanations and targets accessibility while preserving mathematical rigor.