MARC 닫기
00000cam c2200205 c 4500
000005123368
20230227150154
230203s2016 us a b 001c0 eng
▼a 2016022992
▼a 9780262035613 (hardcover : alk. paper)
▼a (KERIS)REF000018313752
▼a DLC
▼b eng
▼c DLC
▼d DLC
▼d 211070
▼a pcc
▼a Q325.5
▼a Q325.5
▼b G651
▼a Deep learning /
▼d Ian Goodfellow,
▼e Yoshua Bengio,
▼e Aaron Courville
▼a Cambridge, Massachusetts :
▼b The MIT Press,
▼c 2016
▼a 775 p. :
▼b ill. ;
▼c 24 cm
▼a Adaptive computation and machine learning
▼a Includes bibliographical references (pages 711-766) and index
▼a 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
▼a Machine learning
▼a Goodfellow, Ian,
▼e author
▼a Bengio, Yoshua,
▼e author
▼a Courville, Aaron,
▼e author
▼b \37000
| 자료유형 : | 단행본 |
|---|---|
| ISBN : | 9780262035613 (hardcover : alk. paper) |
| 분류기호 : | Q325.5 |
| 서명/저자사항 : | Deep learning / Ian Goodfellow, Yoshua Bengio, Aaron Courville |
| 발행사항 : | Cambridge, Massachusetts : The MIT Press, 2016 |
| 형태사항 : | 775 p. : ill. ; 24 cm |
| 총서사항 : | Adaptive computation and machine learning |
| 서지주기 : | Includes bibliographical references (pages 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 |
| 일반주제명 : | Machine learning -- |
| 개인저자 : | Goodfellow, Ian, author |
| 개인저자 : | Bengio, Yoshua, author |
| 개인저자 : | Courville, Aaron, author |
| 언어 | 영어 |
서평쓰기