"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.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/jmtomczak/intro_dgm |
| Publication time | Sun, 17 May 2026 00:55:11 +0000 |
| Retrieval time | 2026-05-17T01:10:19.091Z |
| Last seen | 2026-05-17T01:10:19.091Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | xCkWaHtXNhNy |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.