Elements of Deep Learning
暫譯: 深度學習要素

Ghojogh, Benyamin, Ghodsi, Ali

Elements of Deep Learning
  • 出版商: Springer
  • 出版日期: 2026-05-08
  • 售價: $5,010
  • 貴賓價: 9.5 折 $4,759
  • 語言: 英文
  • 頁數: 567
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3032107377
  • ISBN-13: 9783032107374
  • 相關分類: DeepLearning

    • 海外代購書籍(需單獨結帳)

商品描述

This textbook offers a comprehensive introduction to deep learning and neural networks, integrating core foundations with the latest advances. It begins with essential machine learning concepts and classic neural network architectures before progressing through convolutional models, backpropagation, regularization, generalization theory, PAC learning, and Boltzmann machines. Advanced chapters cover sequence models -- including recurrent networks, LSTMs, attention, Transformers, state-space models, and large language models -- alongside deep generative approaches such as VAEs, GANs, and diffusion models. Emerging topics include graph neural networks, self-supervised learning, metric learning, reinforcement learning, meta-learning, model compression, and knowledge distillation.

Balancing mathematical rigor with hands-on practice, Elements of Deep Learning emphasizes both theoretical depth and real-world application. Different theories are introduced with PyTorch-based code examples, helping readers to translate theory into implementation. Organized into five sections--fundamentals, sequence models, generative models, emerging topics, and practice--the text provides a unified roadmap for mastering modern deep learning.

Designed for advanced undergraduates, graduate students, instructors, and professionals in engineering, computer science, mathematics, and related fields, this book serves both as a primary course text and a reliable reference. With minimal prerequisites in linear algebra and calculus, it offers accessible explanations while equipping readers with practical tools for applications in vision, language, signal processing, healthcare, and beyond.

商品描述(中文翻譯)

這本教科書提供了深度學習和神經網絡的全面介紹，將核心基礎與最新進展相結合。它從基本的機器學習概念和經典的神經網絡架構開始，然後進一步探討卷積模型、反向傳播、正則化、泛化理論、PAC學習和玻爾茲曼機。進階章節涵蓋序列模型，包括遞迴網絡、長短期記憶網絡（LSTMs）、注意力機制、變壓器（Transformers）、狀態空間模型和大型語言模型，以及深度生成方法，如變分自編碼器（VAEs）、生成對抗網絡（GANs）和擴散模型。新興主題包括圖神經網絡、自我監督學習、度量學習、強化學習、元學習、模型壓縮和知識蒸餾。

《深度學習要素》在數學嚴謹性與實踐操作之間取得平衡，強調理論深度和現實應用。不同的理論通過基於PyTorch的代碼示例進行介紹，幫助讀者將理論轉化為實現。該書分為五個部分——基礎、序列模型、生成模型、新興主題和實踐——提供了一個統一的路線圖，以掌握現代深度學習。

本書旨在為高年級本科生、研究生、講師以及工程、計算機科學、數學和相關領域的專業人士服務，既可作為主要課程教材，也可作為可靠的參考資料。該書對線性代數和微積分的前置知識要求最低，提供易於理解的解釋，同時為讀者提供在視覺、語言、信號處理、醫療保健等領域應用的實用工具。

作者簡介

Benyamin Ghojogh received the B.Sc. degree in electrical engineering from the Amirkabir University of Technology, Tehran, Iran, in 2015, the M.Sc. degree in electrical engineering from the Sharif University of Technology, Tehran, Iran, in 2017, and Ph.D. in electrical and computer engineering (in the area of pattern analysis and machine intelligence) from the University of Waterloo, Waterloo, ON, Canada, in 2021. He was a postdoctoral fellow, focusing on machine learning, at the University of Waterloo, in 2021. He is the co-author of Elements of Dimensionality Reduction and Manifold Learning, published by Springer. His research interests include machine learning, deep learning, dimensionality reduction, data science, and computer vision.

Ali Ghodsi is a Professor of Statistics and Computer Science at the University of Waterloo, Director of the Data Science Lab, and a Faculty Affiliate at the Vector Institute for Artificial Intelligence. His research focuses on the theoretical foundations and algorithmic development of machine learning and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision.

He is the co-author of Elements of Dimensionality Reduction and Manifold Learning (Springer). His widely viewed online lectures -- including a popular deep learning course -- make advanced AI topics accessible to a global audience.

作者簡介(中文翻譯)

Benyamin Ghojogh 於2015年獲得伊朗德黑蘭阿米爾卡比爾科技大學的電機工程學士學位，於2017年獲得伊朗德黑蘭沙里夫科技大學的電機工程碩士學位，並於2021年獲得加拿大安大略省滑鐵盧大學的電機與計算機工程博士學位（專注於模式分析與機器智能）。他於2021年在滑鐵盧大學擔任專注於機器學習的博士後研究員。他是由Springer出版的降維與流形學習的元素的共同作者。他的研究興趣包括機器學習、深度學習、降維、數據科學和計算機視覺。

Ali Ghodsi 是滑鐵盧大學的統計學與計算機科學教授，數據科學實驗室主任，以及人工智慧向量研究所的教職員夥伴。他的研究專注於機器學習和人工智慧的理論基礎及算法開發，應用於自然語言處理、生物資訊學和計算機視覺。

他是降維與流形學習的元素（Springer）的共同作者。他的在線講座廣受歡迎，包括一門受歡迎的深度學習課程，使高級人工智慧主題對全球觀眾變得可及。

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