Deep Learning: Foundations and Concepts (Hardcover)
暫譯: 深度學習：基礎與概念 (精裝版)

Christopher M. Bishop , Hugh Bishop

Deep Learning: Foundations and Concepts (Hardcover)
  • 出版商: Springer
  • 出版日期: 2023-11-02
  • 售價: $3,480
  • 貴賓價: 9.5 折 $3,306
  • 語言: 英文
  • 頁數: 649
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031454677
  • ISBN-13: 9783031454677
  • 相關分類: DeepLearning
    • 銷售排行: 🥉 2024/6 英文書 銷售排行 第 3 名

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商品描述

Deep Learning: Foundations and Concepts aims to offer both newcomers to machine learning and those already experienced in the field a comprehensive grasp of fundamental ideas underpinning deep learning. Covering key concepts related to contemporary deep learning architectures and techniques, this essential book will equip readers with a robust foundation for potential future specialization. The field of deep learning is undergoing rapid evolution. Rather than summarizing the latest research developments, Bishop distills the key ideas in order to ensure that the foundations and concepts presented in this book will endure the test of time.

For enhanced accessibility, the book is organized into numerous bite-sized chapters, each exploring a distinct topic. The narrative follows a linear progression, with each chapter building upon content from its predecessors. This structure lends itself effectively to teaching a two-semester undergraduate or postgraduate machine learning course, while remaining equally relevant to those engaged in active research or in self-study.

 

To fully grasp machine learning, a certain level of mathematical understanding is required. The book provides a self-contained introduction to probability theory, and includes appendices summarizing useful results in linear algebra, calculus of variations, and Lagrange multipliers. However, the focus of the book is on conveying a clear understanding of ideas rather than mathematical rigor, with emphasis on real-world practical value of techniques rather than abstract theory. Complex concepts are presented from multiple perspectives including textual descriptions, diagrams, mathematical formulae, and pseudo-code to cater to readers from diverse backgrounds.

 

This book can be viewed as a successor to Neural Networks for Pattern Recognition (Bishop, 1995a) which provided the first comprehensive treatment of neural networks from a statistical perspective. It can be considered as a companion volume to Pattern Recognition and Machine Learning (Bishop, 2006) which covered a broader range of topics in machine learning but predates the deep learning revolution.

 

 

 

商品描述(中文翻譯)

《深度學習：基礎與概念》旨在為機器學習的新手以及已在該領域有經驗的人士提供對深度學習基本理念的全面理解。本書涵蓋與當代深度學習架構和技術相關的關鍵概念，將為讀者提供堅實的基礎，以便未來可能的專業化。深度學習領域正在迅速演變。比起總結最新的研究發展，Bishop 提煉出關鍵思想，以確保本書所呈現的基礎和概念能經得起時間的考驗。

為了增強可讀性，本書被組織成多個小章節，每個章節探討一個獨特的主題。敘述遵循線性進展，每個章節都建立在前一章的內容之上。這種結構非常適合教授為期兩學期的本科或研究生機器學習課程，同時對於從事活躍研究或自學的人士同樣具有相關性。

要充分理解機器學習，需要具備一定程度的數學理解。本書提供了自成體系的概率論介紹，並包括附錄總結線性代數、變分法和拉格朗日乘數的有用結果。然而，本書的重點在於傳達清晰的思想理解，而非數學的嚴謹，強調技術的實際應用價值而非抽象理論。複雜的概念從多個角度呈現，包括文字描述、圖表、數學公式和偽代碼，以迎合不同背景的讀者。

本書可以視為《模式識別的神經網絡》（Bishop, 1995a）的後續作品，該書從統計的角度提供了神經網絡的首次全面處理。它也可以被視為《模式識別與機器學習》（Bishop, 2006）的伴隨卷，後者涵蓋了更廣泛的機器學習主題，但在深度學習革命之前出版。

作者簡介

Chris Bishop is a Technical Fellow at Microsoft and is the Director of Microsoft Research AI4Science. He is a Fellow of Darwin College, Cambridge, a Fellow of the Royal Academy of Engineering, a Fellow of the Royal Society of Edinburgh, and a Fellow of the Royal Society of London. He is a keen advocate of public engagement in science, and in 2008 he delivered the prestigious Royal Institution Christmas Lectures, established in 1825 by Michael Faraday, and broadcast on prime-time national television. Chris was a founding member of the UK AI Council and was also appointed to the Prime Minister's Council for Science and Technology.

Hugh Bishop is an Applied Scientist at Wayve, an end-to-end deep learning based autonomous driving company in London, where he designs and trains deep neural networks. Before working at Wayve, he completed his MPhil in Machine Learning and Machine Intelligence in the engineering department at Cambridge University. Hugh also holds an MEng in Computer Science from the University of Durham, where he focused his projects on deep learning. During his studies, he also worked as an intern at FiveAI, another autonomous driving company in the UK, and as a Research Assistant, producing educational interactive iPython notebooks for machine learning courses at Cambridge University.

 

作者簡介(中文翻譯)

克里斯·比肖普是微軟的技術研究員，並擔任微軟研究AI4Science的主任。他是劍橋大學達爾文學院的研究員、英國皇家工程院的院士、愛丁堡皇家學會的院士，以及倫敦皇家學會的院士。他熱衷於科學的公共參與，並於2008年發表了享有盛譽的皇家學會聖誕講座，該講座由邁克爾·法拉第於1825年創立，並在黃金時段的全國電視上播出。克里斯是英國人工智慧委員會的創始成員，並被任命為首相科學與技術委員會的成員。

休·比肖普是Wayve的應用科學家，Wayve是一家位於倫敦的端到端深度學習自駕車公司，他在那裡設計和訓練深度神經網絡。在Wayve工作之前，他在劍橋大學的工程系完成了機器學習和機器智能的碩士研究（MPhil）。休還擁有達勒姆大學的計算機科學碩士學位（MEng），在那裡他專注於深度學習的項目。在學習期間，他還曾在英國的另一家自駕車公司FiveAI擔任實習生，並作為研究助理，為劍橋大學的機器學習課程製作教育互動的iPython筆記本。

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