MARC record from Internet Archive

LEADER: 05230cam 2200685 i 4500
001 ocn955778308
003 OCoLC
005 20220509201100.0
008 160613t20162016maua b 001 0 eng
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019 $a951226949$a964650355$a1009034043$a1121620891$a1166953339
020 $a0262035618$q(hardcover ;$qalkaline paper)
020 $a9780262035613$q(hardcover ;$qalkaline paper)
035 $a(OCoLC)955778308$z(OCoLC)951226949$z(OCoLC)964650355$z(OCoLC)1009034043$z(OCoLC)1121620891$z(OCoLC)1166953339
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050 00 $aQ325.5$b.G66 2016
060 4 $aQ 325.5
072 7 $aCOM$2eflch
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082 00 $a006.3/1$223
100 1 $aGoodfellow, Ian,$eauthor.
245 10 $aDeep learning /$cIan Goodfellow, Yoshua Bengio and Aaron Courville.
264 1 $aCambridge, Massachusetts :$bThe MIT Press,$c[2016]
264 4 $c©2016
300 $axxii, 775 pages :$billustrations (some color) ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
386 $mGender group:$ngdr$aMen$2lcdgt
386 $mOccupational/field of activity group:$nocc$aUniversity and college faculty members$2lcdgt
490 1 $aAdaptive computation and machine learning
504 $aIncludes bibliographical references (pages 711-766) and index.
505 00 $gIntroduction --$tApplied math and machine learning basics.$tLinear algebra --$tProbability and information theory --$tNumerical computation --$tMachine learning basics --$tDeep networks: modern practices.$tDeep feedforward networks --$tRegularization for deep learning --$tOptimization for training deep models --$tConvolutional networks --$tSequence modeling: recurrent and recursive nets --$tPractical methodology --$tApplications --$tDeep learning research.$tLinear factor models --$tAutoencoders --$tRepresentation learning --$tStructured probabilistic models for deep learning --$tMonte Carlo methods --$tConfronting the partition function --$tApproximate inference --$tDeep generative models.
520 $a"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 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"--Page 4 of cover.
650 0 $aMachine learning.
650 2 $aMachine Learning
650 6 $aApprentissage automatique.
650 7 $aComputers and IT.$2eflch
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
650 7 $aMaschinelles Lernen$2gnd
650 7 $aMachine learning.$2nli
650 7 $aComputers and IT.$2ukslc
700 1 $aBengio, Yoshua,$eauthor.
700 1 $aCourville, Aaron,$eauthor.
830 0 $aAdaptive computation and machine learning.
856 42 $uhttp://www.deeplearningbook.org/
938 $aBrodart$bBROD$n116994169
938 $aBaker and Taylor$bBTCP$nBK0018933607
938 $aYBP Library Services$bYANK$n13016024
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948 $hHELD BY P4A - 603 OTHER HOLDINGS