"Deep Generative Modeling": Introductory Examples
The book 'Deep Generative Modeling' provides a comprehensive overview of various deep generative models and their applications. It is aimed at students, engineers, and researchers with a basic understanding of mathematics and programming. The text includes practical examples and code snippets to facilitate learning and experimentation with deep generative models.
- ▪The book covers major classes of deep generative models including GANs, flow-based models, and large language models.
- ▪It is designed for readers with a modest mathematical background in calculus, linear algebra, and probability theory.
- ▪Practical examples are provided in Jupyter notebooks to help readers understand and implement deep generative models.
Opening excerpt (first ~120 words) tap to expand
"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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.