Jakub M. Tomczak 
Deep Generative Modeling [EPUB ebook] 

支持

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 Py Torch (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 Git Hub 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.

€64.40
支付方式
购买此电子书可免费获赠一本!
语言 英语 ● 格式 EPUB ● ISBN 9783031640872 ● 出版者 Springer International Publishing ● 发布时间 2024 ● 下载 3 时 ● 货币 EUR ● ID 9945593 ● 复制保护 Adobe DRM
需要具备DRM功能的电子书阅读器

来自同一作者的更多电子书 / 编辑

49,595 此类电子书