02481 a2200241 4500999001700000008004100017020001800058082001600076100002000092245001800112260003400130300003300164365002200197490004600219504005100265520169100316650002102007650001802028700001902046700002102065942001202086952014102098 c29642d29642220627b xxu||||| |||| 00| 0 eng d a9780262035613 a006.31bGOO aGoodfellow, Ian aDeep learning bMIT Press,c2016aCambridge : axxii, 775 p. ;bill.,c24 cm b80.00cUSDd81.20 aAdaptive computation and machine learning aIncludes bibliographical references and index. aDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. aMachine learning aDeep learning aBengio, Yoshua aCourville, Aaron 2ddccBK 00102ddc406006_310000000000000_GOO70940974aDAIICTbDAIICTd2022-06-25g6496.00l13o006.31 GOOp033141r2023-07-25s2023-05-24yCR