Deep Generative Modeling - Jakub M. Tomczak

This First Comprehensive Book On Models Behind Generative Ai Has Been Thoroughly Revised To Cover All Major Classes Of Deep Generative Models: Mixture Models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, Gans, Hybrid Models, Score-based Generative Models, Energy-based Models, And Large Language Models. In Addition, Generative Ai Systems Are Discussed, Demonstrating How Deep Generative Models Can Be For Neural Compression, Among Others. Deep Generative Modeling Is Designed To Appeal To Curious Students, Engineers, And Researchers With A Modest Mathematical Background In Undergraduate Calculus, Linear Algebra, Probability Theory, And The Basics Of Machine Learning, Deep Learning, And Programming In Python And Pytorch (or Other Deep Learning Libraries).