Neural Networks and Deep Learning: A Textbook (Paperback)
Aggarwal, Charu C.
- 出版商: Springer
- 出版日期: 2024-07-01
- 售價: $2,010
- 貴賓價: 9.5 折 $1,910
- 語言: 英文
- 頁數: 529
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031296443
- ISBN-13: 9783031296444
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相關分類:
DeepLearning
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商品描述
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. 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.
作者簡介
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 AchievementAwards (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 DataMining 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.
作者簡介(中文翻譯)
Charu C. Aggarwal 是位於紐約約克鎮的 IBM T. J. Watson 研究中心的傑出研究人員（Distinguished Research Staff Member, DRSM）。他於 1993 年在印度理工學院坎普爾校區獲得計算機科學學士學位，並於 1996 年在麻省理工學院獲得博士學位。他在資料挖掘領域有廣泛的研究經驗，已在經過審核的會議和期刊上發表超過 400 篇論文，並擁有超過 80 項專利。他是 20 本書籍的作者或編輯，包括資料挖掘、推薦系統和異常分析的教科書。由於其專利的商業價值，他三度被 IBM 指定為大師發明家（Master Inventor）。他曾因在資料流中的生物恐怖威脅檢測工作而獲得 IBM 企業獎（2003），因對隱私技術的科學貢獻而獲得 IBM 傑出創新獎（2008），以及因在資料流/高維資料方面的工作而獲得兩次 IBM 傑出技術成就獎（2009、2015）。他因在基於凝聚的隱私保護資料挖掘方面的工作而獲得 EDBT 2014 時間考驗獎。他是 IEEE ICDM 研究貢獻獎（2015）和 ACM SIGKDD 創新獎的獲得者，這兩項獎項是資料挖掘領域中對有影響力的研究貢獻的最具聲望的獎項。他還獲得了 W. Wallace McDowell 獎，這是 IEEE 計算機學會在計算機科學領域頒發的最高獎項。
他曾擔任 IEEE 大數據會議（2014）的共同主席，以及 ACM CIKM 會議（2015）、IEEE ICDM 會議（2015）和 ACM KDD 會議（2016）的程序共同主席。他於 2004 年至 2008 年擔任 IEEE 知識與資料工程期刊的副編輯，目前是 IEEE 大數據期刊的副編輯、資料挖掘與知識發現期刊的行動編輯，以及知識與資訊系統期刊的副編輯。他曾擔任 ACM 資料發現交易的主編以及 ACM SIGKDD Explorations 的主編，並且是 ACM Books 的主編。他在 Springer 出版的社交網絡講義筆記的諮詢委員會中任職。他曾擔任 SIAM 資料挖掘活動小組的副主席，並且是 SIAM 工業委員會的成員。他是 SIAM、ACM 和 IEEE 的會士，因其對知識發現和資料挖掘演算法的貢獻而獲得此榮譽。
