000			01566nam a22001577a 4500
008			180820b xxu||||| |||| 00| 0 eng d
020			_a978-0-262-03561-3
082			_a006.3/GOO/BEN
100			_aGOODFELLOW, IAN
245			_aDEEP LEARNING
260			_aU.S _bMIT PRESS _c2016
300			_axxii,775
520			_aThis 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 videogames. 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
942			_2ddc _cB
999			_c603454 _d603454