
000 | 00000cam u2200205 a 4500 | |
001 | 000045892084 | |
005 | 20221123181739 | |
008 | 170105s2016 maua b 001 0 eng d | |
010 | ▼a 2016022992 | |
020 | ▼a 9780262035613 (hardcover : alk. paper) | |
035 | ▼a (KERIS)REF000018128489 | |
040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009 | |
050 | 0 0 | ▼a Q325.5 ▼b .G66 2017 |
082 | 0 0 | ▼a 006.3/1 ▼2 23 |
084 | ▼a 006.31 ▼2 DDCK | |
090 | ▼a 006.31 ▼b G651d | |
100 | 1 | ▼a Goodfellow, Ian. |
245 | 1 0 | ▼a Deep learning / ▼c Ian Goodfellow, Yoshua Bengio, and Aaron Courville. |
260 | ▼a Cambridge, MA : ▼b MIT Press, ▼c c2016. | |
300 | ▼a xxii, 775 p. : ▼b ill. (some col.) ; ▼c 24 cm. | |
490 | 1 | ▼a Adaptive computation and machine learning series |
504 | ▼a Includes bibliographical references and index. | |
650 | 0 | ▼a Machine learning. |
700 | 1 | ▼a Bengio, Yoshua, ▼d 1964- ▼0 AUTH(211009)147818. |
700 | 1 | ▼a Courville, Aaron ▼0 AUTH(211009)147819. |
830 | 0 | ▼a Adaptive computation and machine learning series. |
945 | ▼a KLPA |
소장정보
No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
---|---|---|---|---|---|---|---|
No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 G651d | 등록번호 121238616 | 도서상태 대출중 | 반납예정일 2023-10-11 | 예약 | 서비스 |
No. 2 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 G651d | 등록번호 121241706 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
No. 3 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 G651d | 등록번호 121245251 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
No. 4 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 G651d | 등록번호 121260695 | 도서상태 대출중 | 반납예정일 2023-10-14 | 예약 | 서비스 |
컨텐츠정보
저자소개
목차
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.