Deep Generative Modeling 2/E (深度生成模型（第二版）)

Tomczak, Jakub M.

Deep Generative Modeling 2/E
  • 出版商: Springer
  • 出版日期: 2024-09-11
  • 售價: $2,740
  • 貴賓價: 9.5 折 $2,603
  • 語言: 英文
  • 頁數: 300
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031640861
  • ISBN-13: 9783031640865

    • 立即出貨 (庫存=1)

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商品描述

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

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.

 

 

 

商品描述(中文翻譯)

這本關於生成式人工智慧背後模型的首部綜合性書籍已進行全面修訂，涵蓋所有主要類別的深度生成模型：混合模型、概率電路、回歸模型、基於流的模型、潛變量模型、生成對抗網絡（GANs）、混合模型、基於分數的生成模型、基於能量的模型，以及大型語言模型。此外，書中還討論了生成式人工智慧系統，展示了深度生成模型如何用於神經壓縮等應用。

《深度生成建模》旨在吸引對數學有基本背景的好奇學生、工程師和研究人員，這些背景包括本科微積分、線性代數、概率論，以及機器學習、深度學習和Python及PyTorch（或其他深度學習庫）的基礎知識。這本書應該會引起來自計算機科學、工程、數據科學、物理學和生物資訊學等多個領域的學生和研究人員的興趣，特別是那些希望熟悉深度生成建模的人。

為了吸引讀者，書中以具體範例和程式碼片段介紹基本概念。與書籍配套的完整程式碼可在作者的GitHub網站上獲得：github.com/jmtomczak/intro_dgm

這本書的最終目標是概述深度生成建模中最重要的技術，並最終使讀者能夠構建新模型並實現它們。

作者簡介

Jakub M. Tomczak is an associate professor and the head of the Generative AI group at the Eindhoven University of Technology (TU/e). Before joining the TU/e, he was an assistant professor at Vrije Universiteit Amsterdam, a deep learning researcher (Engineer, Staff) in Qualcomm AI Research in Amsterdam, a Marie Sklodowska-Curie individual fellow in Prof. Max Welling's group at the University of Amsterdam, and an assistant professor and a postdoc at the Wroclaw University of Technology. His main research interests include ML, DL, deep generative modeling (GenAI), and Bayesian inference, with applications to image/text processing, Life Sciences, Molecular Sciences, and quantitative finance. He serves as an action editor of "Transactions of Machine Learning Research", and an area chair of major AI conferences (e.g., NeurIPS, ICML, AISTATS). He is a program chair of NeurIPS 2024. He is the author of the book entitled "Deep Generative Modeling", the first comprehensive book on Generative AI. He is also the founder of Amsterdam AI Solutions.

作者簡介(中文翻譯)

Jakub M. Tomczak 是埃因霍溫科技大學 (TU/e) 的副教授及生成式人工智慧小組的負責人。在加入 TU/e 之前，他曾擔任阿姆斯特丹自由大學的助理教授、在高通 (Qualcomm) 阿姆斯特丹的人工智慧研究部門擔任深度學習研究員 (工程師、員工)、在阿姆斯特丹大學的 Max Welling 教授小組中擔任瑪麗·斯克沃多夫斯卡-居里個人研究員，以及在弗羅茨瓦夫科技大學擔任助理教授和博士後研究員。他的主要研究興趣包括機器學習 (ML)、深度學習 (DL)、深度生成建模 (GenAI) 和貝葉斯推斷，並應用於影像/文本處理、生命科學、分子科學和定量金融。他擔任《機器學習研究會報》的行動編輯，並擔任主要人工智慧會議 (例如 NeurIPS、ICML、AISTATS) 的領域主席。他是 NeurIPS 2024 的程式主席。他是名為《深度生成建模》的書籍的作者，這是關於生成式人工智慧的第一本綜合性書籍。他也是阿姆斯特丹人工智慧解決方案的創始人。

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