LEADER 03697oam 2200373 450 001 996320734503316 005 20230323125548.0 010 $a0-262-33737-1 035 $a(MiAaPQ)EBC6287197 035 $a(PPN)25087847X 035 $a(EXLCZ)994560000000000246 100 $a20201210h20162016 uy 0 101 0 $aeng 135 $aurcn#---uuuuu 200 10$aDeep learning /$fIan Goodfellow, Yoshua Bengio and Aaron Courville 210 1$aCambridge, Massachusetts ;$aLondon, England :$cThe MIT Press,$d[2016]. 210 4$dİ2016 215 $a1 online resource (xxii, 775 pages) $cillustrations 225 1 $aAdaptive computation and machine learning 311 0 $a9780262035613 320 $aIncludes bibliographical references (pages 711-766) and index. 327 $aIntroduction -- 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. 330 $aAn introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. 410 0$aAdaptive computation and machine learning. 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aGoodfellow$b Ian$0752902 702 $aBengio$b Yoshua 702 $aCourville$b Aaron 906 $aBOOK 912 $a996320734503316 996 $aDeep learning$93068251 997 $aUNISA