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113330391
Deep Generative Modeling
Item #:
113330391
OMR 22
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- 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.
| Publisher | Springer |
| Publication date | September 11, 2024 |
| Edition | Second Edition 2024 |
| Language | English |
| Print length | 336 pages |
| ISBN-10 | 3031640861 |
| ISBN-13 | 978-3031640865 |
| Item Weight | 1 pounds (450 grams) |
Product Description
Deep Generative Modeling
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Intelligence & Semantics Editorial Review
Deep Generative Modeling, published by Springer, offers an insightful perspective on advanced modeling techniques in deep learning. The second edition, releasing on September 11, 2024, expands on the latest developments and applications across 336 pages, making it a valuable resource for researchers and practitioners alike. It dives deep into the algorithms, theory, and real-world applications that drive generative models. Readers have noted the clarity of explanations and the thoroughness of examples, which enhance understanding for those at various levels of expertise.
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Pros
- Comprehensive coverage of modern generative models
- Clear explanations suitable for various expertise levels
- Updated content reflecting latest research and trends
- Well-structured chapters for easy navigation
- Valuable resource for researchers and practitioners
Cons
- Some readers may find it dense with advanced topics
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