
Neural Networks and Deep Learning
A Textbook
Charu C. Aggarwal(Author)
Springer (Publisher)
1st Edition
Published on 13. September 2018
Book
Hardback
XXIII, 497 pages
978-3-319-94462-3 (ISBN)
Article exhausted; check for reprint
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
1st ed. 2018
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
College/higher education
Illustrations
10
128 s/w Abbildungen, 11 farbige Abbildungen, 10 farbige Tabellen
XXIII, 497 p. 139 illus., 11 illus. in color.
Dimensions
Height: 25.4 cm
Width: 17.8 cm
Weight
1191 gr
ISBN-13
978-3-319-94462-3 (9783319944623)
DOI
10.1007/978-3-319-94463-0
Schweitzer Classification
Other editions
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Book
06/2023
2nd Edition
Springer
€80.24
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Book
01/2019
Springer
€53.49
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E-Book
08/2018
1st Edition
Springer
€53.49
Available for download
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.