Deep Generative Modeling

Jakub M. Tomczak

ISBN 10: 3031640861 / ISBN 13: 9783031640865

Published by Springer International Publishing, Springer International Publishing Sep 2024, 2024

About this Item

Neuware 340 pp. Englisch.

Seller Inventory # 9783031640865

Bibliographic Details

• Title: Deep Generative Modeling
• Publisher: Springer International Publishing, Springer International Publishing Sep 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 3031640861
• ISBN 13: 9783031640865
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 24D millimeters 241 millimeters width by 160 millimeters height by 24 millimeters depth
• Weight: 676 grams

About this title

Synopsis

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_dgm

The 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.