000			03388nam a22003137a 4500
005			20220921160342.0
008			220505b |||||||| |||| 00| 0 eng d
020			_a9780262035613
028			_a:International Book Distributors _b:Flat No 17, Prakash Apartment 4405/2, 5 Ansari Road Darya Ganj New Delhi _c:117716 _d:22/04/2022
082			_a006.31 GOO
100			_aGoodfellow, Ian
245			_aDeep Learning _c/ Ian Goodfellow
260			_aCambridge, Massachusetts _b: The MIT Press _c©2016
300			_axxii, 775p. _b: illustrations some color _c; 24 cm
365			_b6328
490			_aAdaptive computation and machine learning
505			_a Introduction -- Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep 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			_aComputer Science
650			_aSpecial computer methods
650			_aMachine learning.
650			_aApprentissage automatique.
650			_aComputers and IT.
650			_aMaschinelles Lernen
700			_aGoodfellow, Ian
700			_a Bengio, Yoshua
700			_aCourville, Aaron
942			_cBOOK
999			_c59168 _d59168