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Deep learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep learning // Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
作者:	Goodfellow, Ian.
其他作者:	Courville, Aaron.
出版者:	Cambridge, Massachusetts :The MIT Press, : 2016.,
面頁冊數:	xxii, 775 p. :ill. (some color) ; : 25 cm.;
標題:	Machine learning. -
ISBN:	9780262035613 (Cloth) :

Deep learning /
Goodfellow, Ian.

Deep learning /Ian Goodfellow, Yoshua Bengio, and Aaron Courville. - Cambridge, Massachusetts :The MIT Press,2016. - xxii, 775 p. :ill. (some color) ;25 cm. - Adaptive computation and machine learning..

Includes bibliographical references (p. 711-766) and index.

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 (Cloth) :NT2472

LCCN: 2016022992Subjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .G66 2016

Dewey Class. No.: 006.3/1

Deep learning /
LDR:01325cam a2200193 a 4500 001 891216
008 180920s2016 maua b 001 0 eng
010 $a 2016022992
020 $a 9780262035613 (Cloth) : $c NT2472
020 $a 0262035618 (Cloth)
035 $a 19134018
040 $a DLC $b eng $c DLC $d DLC $c NFU
050 0 0 $a Q325.5 $b .G66 2016
082 0 0 $a 006.3/1 $2 23
100 1 $a Goodfellow, Ian. $3 1149465
245 1 0 $a Deep learning / $c Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
260 $a Cambridge, Massachusetts : $b The MIT Press, $c 2016.
300 $a xxii, 775 p. : $b ill. (some color) ; $c 25 cm.
490 0 $a Adaptive computation and machine learning.
504 $a Includes bibliographical references (p. 711-766) and index.
505 0 $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.
650 0 $a Machine learning. $3 561253
700 1 $a Courville, Aaron. $3 1149467
700 1 $a Bengio, Yoshua. $3 1149466

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