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Neural Networks and Deep Learning: A Textbook (Hardcover)
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.
About the Author
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. 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."