000			02573 a2200277 4500
003			OSt
005			20250410020503.0
008			191120b xxu||||| |||| 00| 0 eng d
020			_a9780262035613 (hbk. ): _cUSD 80.00
040			_cIISER- BPR _dIISER- BPR
082			_223rd _a006.31 _bGOO/D
100			_aGoodfellow, Ian _9488
222			_aCOMPUTER SCIENCE
245			_aDeep learning/ _c[by] Ian Goodfellow, Yoshua Bengio and Aaron Courville
260			_aCambridge: _bMIT Press, _cc2016
300			_axxii, 775 p.: _bill.; _c24 cm.
440			_aAdaptive computation and machine learning. _9489
500			_aIncludes bibliography and index.
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 _9490
650			_aComputer Science _9491
650			_aInformation Technology _9492
700			_aBengio, Yoshua _9493
700			_aCourville, Aaron _9494
942			_2ddc _cBK _05
999			_c1157 _d1157