Neural Networks and Deep Learning: A Textbook, 2/e (Hardcover)

This textbook covers both classical and modern models in deep learning and includes examples and exercises throughout the chapters. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories:

The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2.

Many traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks.

Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines.

Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12.

The textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.

Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

The second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.

Greater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.

這本教科書涵蓋了深度學習中的經典和現代模型，並在各章節中提供了例子和練習。書中詳細介紹了用於文本、圖像和圖形等各種數據領域的深度學習方法。本書的章節分為三個類別：

1. 神經網絡的基礎：第2章討論了反向傳播算法。許多傳統的機器學習模型可以理解為神經網絡的特殊情況。第3章探討了傳統機器學習和神經網絡之間的聯繫。支持向量機、線性/邏輯回歸、奇異值分解、矩陣分解和推薦系統都被證明是神經網絡的特殊情況。

2. 神經網絡的基礎知識：第4章和第5章詳細討論了訓練和正則化。第6章和第7章介紹了基於徑向基函數（RBF）的網絡和受限玻爾茨曼機。

3. 神經網絡的高級主題：第8章、第9章和第10章討論了循環神經網絡、卷積神經網絡和圖神經網絡。第11章和第12章介紹了深度強化學習、注意機制、Transformer網絡、Kohonen自組織映射和生成對抗網絡等幾個高級主題。

這本教科書是為研究生和高年級本科生撰寫的。在相關領域工作的研究人員和從業人員也會想要購買這本書。在可能的情況下，強調應用為中心的觀點，以便理解每個技術類別的實際應用。

第二版在組織和擴展方面進行了大幅調整，增加了關於反向傳播和圖神經網絡的獨立章節。許多章節在第一版的基礎上進行了重大修訂。更加注重現代深度學習思想，如注意機制、Transformer和預訓練語言模型。

Charu C. Aggarwal is a Distinguished Research Staff Member(DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining. He has published more than 400 papers in refereed conferences and journals and authored over 80 patents. He is the author or editor of 20 books, including textbooks on data mining, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of two IBM Outstanding Technical Achievement Awards (2009, 2015) for his work on data streams/high-dimensional data. He received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He is a recipient of the IEEE ICDM Research Contributions Award (2015) and ACM SIGKDD Innovation Award, which are the two most prestigious awards for influential research contributions in the field of data mining. He is also a recipient of the W. Wallace McDowell Award, which is the highest award given solely by the IEEE Computer Society across the field of Computer Science.
He has served as the general co-chair of the IEEE Big Data Conference (2014) and as the program co-chair of the ACM CIKM Conference (2015), the IEEE ICDM Conference (2015), and the ACM KDD Conference (2016). He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the IEEE Transactions on Big Data, an action editor of the Data Mining and Knowledge Discovery Journal, and an associate editor of the Knowledge and Information System Journal. He has served or currently serves as the editor-in-chief of the ACM Transactions on Knowledge Discovery from Data as well as the ACM SIGKDD Explorations. He is also an editor-in-chief of ACM Books. He serves on the advisory board of the Lecture Notes on Social Networks, a publication by Springer. He has served as the vice-president of the SIAM Activity Group on Data Mining and is a member of the SIAM industry committee. He is a fellow of the SIAM, ACM, and the IEEE, for "contributions to knowledge discovery and data mining algorithms.

Charu C. Aggarwal是紐約約克鎮IBM T. J. Watson研究中心的傑出研究員。他於1993年在印度坎普爾的印度理工學院獲得計算機科學的學士學位，並於1996年在麻省理工學院獲得博士學位。他在數據挖掘領域有豐富的工作經驗。他在被審查的會議和期刊上發表了400多篇論文，並擁有80多項專利。他是20本書的作者或編輯，包括關於數據挖掘、推薦系統和異常值分析的教科書。由於他的專利具有商業價值，他曾三次被IBM指定為大師發明家。他因在數據流中的生物恐怖主義威脅檢測工作而獲得IBM企業獎（2003年），因對隱私技術的科學貢獻而獲得IBM傑出創新獎（2008年），並因在數據流/高維數據方面的工作而獲得兩項IBM傑出技術成就獎（2009年、2015年）。他因基於凝聚的隱私保護數據挖掘工作獲得EDBT 2014時間測試獎。他是IEEE ICDM研究貢獻獎（2015年）和ACM SIGKDD創新獎的獲得者，這是數據挖掘領域最具影響力的兩個研究貢獻獎項。他還是IEEE計算機學會頒發的最高獎項W. Wallace McDowell Award的獲得者，該獎項涵蓋了計算機科學領域的所有範疇。他曾擔任IEEE大數據會議（2014年）的總共同主席，以及ACM CIKM會議（2015年）、IEEE ICDM會議（2015年）和ACM KDD會議（2016年）的程序共同主席。他曾擔任IEEE知識與數據工程交易的副編輯（2004年至2008年）。他是IEEE大數據交易的副編輯，Data Mining and Knowledge Discovery Journal的行動編輯，以及Knowledge and Information System Journal的副編輯。他曾擔任ACM Transactions on Knowledge Discovery from Data和ACM SIGKDD Explorations的總編輯。他還是ACM Books的總編輯。他是Springer出版的Lecture Notes on Social Networks的顧問委員會成員。他曾擔任SIAM數據挖掘活動小組的副主席，並是SIAM工業委員會的成員。他是SIAM、ACM和IEEE的會士，以表彰他在知識發現和數據挖掘算法方面的貢獻。