Libro ben scritto, ma purtroppo non basta da solo, ci sono i libri più completi.

This is a clear, concise introduction to generative modeling with many useful figures. The print edition is about 200 pages and gives a surprising amount of information. It covers various types of generative models such as Autoregressive models (WaveNet), Normalizing flows, VAEs, and GANs. Normalizing flows are probably not as well known as others and its good to have them in one place. The book also covers some interesting applications such as neural compression. It's not a book of recipe's and the examples are simple, generally from Sklearn digits data set. They're meant to be run on a CPU so more people can try them. However the author does provide some insight into the models and gives several useful references. You do have to know some probability, calculus to follow along but the equations are generally clearly written without much clutter. The Pytorch code is available and can be good starter code to build your own models. If you're just beginning, it will probably not be the only book you get but is definitely a good addition.