소장자료
| LDR | 01597cam a2200289 a 4500 | ||
| 001 | 0093754617▲ | ||
| 005 | 20180520002442▲ | ||
| 008 | 160613t20162016maua b 001 0 eng c▲ | ||
| 010 | ▼a2016022992▲ | ||
| 020 | ▼a9780262035613 (hardcover : alk. paper)▲ | ||
| 020 | ▼a0262035618 (hardcover : alk. paper)▲ | ||
| 040 | ▼aDLC▼beng▼cDLC▼dDLC▼d221016▲ | ||
| 042 | ▼apcc▲ | ||
| 050 | 0 | 0 | ▼aQ325.5▼b.G66 2016▲ |
| 082 | 0 | 0 | ▼a006.3/1▼223▲ |
| 090 | ▼a006.31▼bG651d▲ | ||
| 100 | 1 | ▼aGoodfellow, Ian.▲ | |
| 245 | 1 | 0 | ▼aDeep learning /▼cIan Goodfellow, Yoshua Bengio, and Aaron Courville.▲ |
| 260 | ▼aCambridge, Massachusetts :▼bThe MIT Press,▼c2016.▲ | ||
| 300 | ▼axxii, 775 p. :▼bill. ;▼c24 cm.▲ | ||
| 490 | 0 | ▼aAdaptive computation and machine learning▲ | |
| 504 | ▼aIncludes bibliographical references (p. 711-766) and index.▲ | ||
| 505 | 0 | ▼aApplied 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.▲ | |
| 650 | 0 | ▼aMachine learning,▲ | |
| 700 | 1 | ▼aBengio, Yoshua.▲ | |
| 700 | 1 | ▼aCourville, Aaron.▲ | |
| 999 | ▼a김진영▼c김정이▲ |
Deep learning
자료유형
국외단행본
서명/책임사항
Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
발행사항
Cambridge, Massachusetts : The MIT Press , 2016.
형태사항
xxii, 775 p. : ill. ; 24 cm.
서지주기
Includes bibliographical references (p. 711-766) and index.
내용주기
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.
ISBN
9780262035613 (hardcover : alk. paper) 0262035618 (hardcover : alk. paper)
청구기호
006.31 G651d
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