
Neural Networks and Deep Learning
A Textbook
Charu C. Aggarwal(Author)
Springer (Publisher)
Published on 31. January 2019
Book
Paperback/Softback
XXIII, 497 pages
978-3-030-06856-1 (ISBN)
Description
Reviews / Votes
"The book recommends itself as a stepping-stone of the research-intensive area of deep learning and a worthy continuation of the previous textbooks written by the author . . Thanks to its systematic and thorough approach complemented with the variety of resources (bibliographic and software references, exercises) neatly presented after each chapter, it is suitable for audiences of varied expertise or background." (Irina Ioana Mohorianu, zbMATH 1402.68001, 2019)More details
Edition
Softcover Reprint of the Original 1st 2018 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
11
128 s/w Abbildungen, 11 farbige Abbildungen
XXIII, 497 p. 139 illus., 11 illus. in color.
Dimensions
Height: 25.4 cm
Width: 17.8 cm
Weight
986 gr
ISBN-13
978-3-030-06856-1 (9783030068561)
DOI
10.1007/978-3-319-94463-0
Schweitzer Classification
Other editions
Additional editions

Book
09/2018
1st Edition
Springer
€69.54
Article exhausted; check for reprint
Person
Charu C. Aggarwal is a Distinguished Research Sta? 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. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 350 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 18 books, including textbooks on data mining, machine learning (for text), recommender systems, and outlier analy-sis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several inter-nal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). Aside from serving as program or general chair of many major conferences in data mining, he is an editor-in-chief of the ACM SIGKDD Explorations and also of the ACM Transactions on Knowledge Discovery from Data. He is a fellow of the SIAM, ACM, and the IEEE, for "contributions to knowledge discovery and data mining algorithms."
Content
1 An Introduction to Neural Networks.- 2 Machine Learning with Shallow Neural Networks.- 3 Training Deep Neural Networks.- 4 Teaching Deep Learners to Generalize.- 5 Radical Basis Function Networks.- 6 Restricted Boltzmann Machines.- 7 Recurrent Neural Networks.- 8 Convolutional Neural Networks.- 9 Deep Reinforcement Learning.- 10 Advanced Topics in Deep Learning.