000			03093nam a22002177a 4500
005			20221015171228.0
008			211027b ||||| |||| 00| 0 eng d
020			_a9780262035613
082			_a006.31 _bGOO
100			_aGoodfellow, Ian;
245			_aDeep learning _cIan Goodfellow; Yoshua Bengio; Aaron Courville
260			_aCambridge, Massachusetts : _bThe MIT Press, _c©2016.
300			_axxii, 775 pages : _billustrations (some color) ; _c24 cm.
505			_tApplied math and machine learning basics. Linear algebra -- _tProbability and information theory -- _tNumerical computation -- _tMachine learning basics -- _tDeep networks: modern practices. Deep feedforward networks -- _tRegularization for deep learning -- _tOptimization for training deep models -- _tConvolutional networks -- _tSequence modeling: recurrent and recursive nets -- _tPractical methodology -- _tApplications -- _tDeep learning research. Linear factor models -- _tAutoencoders -- _tRepresentation learning -- _tStructured probabilistic models for deep learning -- _tMonte Carlo methods -- _tConfronting the partition function -- _tApproximate inference -- _tDeep generative models.
520			_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
650			_aMachine learning
700			_aBengio, Yoshua
700			_aCourville, Aaron
856			_uhttps://archive.org/details/deeplearning0000good/page/n9/mode/2up _yEbooks
942			_2ddc _cBK
999			_c1214 _d1214