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Fundamentals of deep learning :desig...

Buduma, Nikhil.

Fundamentals of deep learning :designing next-generation machine intelligence algorithms /

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	006.3/1
[NT 47271] Title/Author:	Fundamentals of deep learning : : designing next-generation machine intelligence algorithms // Nikhil Buduma ; with contributions by Nicholas Locascio.
[NT 51403] remainder title:	Designing next-generation machine intelligence algorithms
作者:	Buduma, Nikhil.
出版者:	Sebastopol, CA : : O'Reilly Media,, 2017.
面页册数:	xii, 283 p. : : ill. (some col.) ;; 24 cm.
标题:	Artificial intelligence.
标题:	Machine learning.
标题:	Neural networks (Computer science)
ISBN:	9781491925614 (pbk.) :
[NT 15000227] null:	Includes bibliographical references and index.
[NT 15000228] null:	The neural network -- Training feed-forward neural networks -- Implementing neural networks in TensorFlow -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Memory augmented neural networks -- Deep reinforcement learning.

Fundamentals of deep learning :designing next-generation machine intelligence algorithms /
Buduma, Nikhil.

Fundamentals of deep learning :designing next-generation machine intelligence algorithms /Designing next-generation machine intelligence algorithmsNikhil Buduma ; with contributions by Nicholas Locascio. - 1st ed. - Sebastopol, CA :O'Reilly Media,2017. - xii, 283 p. :ill. (some col.) ;24 cm.

Includes bibliographical references and index.

The neural network -- Training feed-forward neural networks -- Implementing neural networks in TensorFlow -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Memory augmented neural networks -- Deep reinforcement learning.

ISBN: 9781491925614 (pbk.) :NTD 1,382Subjects--Topical Terms:

172060
Artificial intelligence.

LC Class. No.: TA347.A78 / B83 2017

Dewey Class. No.: 006.3/1

Fundamentals of deep learning :designing next-generation machine intelligence algorithms /
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300 $a xii, 283 p. : $b ill. (some col.) ; $c 24 cm.
504 $a Includes bibliographical references and index.
505 0 $a The neural network -- Training feed-forward neural networks -- Implementing neural networks in TensorFlow -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Memory augmented neural networks -- Deep reinforcement learning.
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650 0 $a Machine learning. $3 202931
650 0 $a Neural networks (Computer science) $3 386157