Name: Deep Generative Modeling
SKU: 64823066
Price: 61.61 EUR
Availability: InStock
Author: Jakub M. Tomczak

Type

Physical Book

Publisher

Springer International Publishing AG

Author

Jakub M. Tomczak

Year

2025

Pages

313

Format

Paperback

Dimensions

23.5x15.5 cm

ISBN13

9783031640896

Deep Generative Modeling

Jakub M. Tomczak (Author) · Springer International Publishing AG · Paperback

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Synopsis "Deep Generative Modeling"

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 used 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). It should find interest among students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics who wish to get familiar with deep generative modeling. In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on the author's GitHub site: github.com/jmtomczak/intro_dgmThe ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.