深度生成建模第二版

English | 2024 | ASIN: B0D4TR44GC | 336 pages| Epub PDF (True) | 56 MB

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

中文|2024|ASIN:B0D4TR44GC|336页|Epub PDF（True）|56 MB这本关于生成式人工智能背后模型的第一本综合性书籍已经过彻底修订，涵盖了所有主要类别的深度生成模型：混合模型、概率电路、自回归模型、基于流的模型、潜在变量模型、GAN、混合模型、基于分数的生成模型、基于能量的模型和大型语言模型。此外，还讨论了生成式人工智能系统，展示了深度生成模型如何用于神经压缩等。深度生成建模旨在吸引在本科微积分、线性代数、概率论以及Python和PyTorch（或其他深度学习库）中的机器学习、深度学习和编程基础方面具有适度数学背景的好奇学生、工程师和研究人员。它应该引起来自不同背景的学生和研究人员的兴趣，包括计算机科学、工程、数据科学、物理学和生物信息学，他们希望熟悉深度生成建模。为了吸引读者，这本书通过具体的例子和代码片段介绍了基本概念。本书附带的完整代码可在作者的GitHub网站上找到：GitHub.com/jmtomczak/intro_dgm

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