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Deep learning : = a practitioner's a...

Gibson, Adam.

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Deep learning : = a practitioner's approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep learning :/ Josh Patterson and Adam Gibson.
其他題名:	a practitioner's approach /
作者:	Patterson, Josh.
其他作者:	Gibson, Adam.
出版者:	Sebastopol, CA :O'Reilly, : 2017.,
面頁冊數:	xxi, 507 p. :ill. ;24 cm.
標題:	Machine learning. -
ISBN:	9781491914250

Deep learning : = a practitioner's approach /
Patterson, Josh.

Deep learning :a practitioner's approach /Josh Patterson and Adam Gibson. - 1st ed. - Sebastopol, CA :O'Reilly,2017. - xxi, 507 p. :ill. ;24 cm.

Includes bibliographical references and index.

A review of machine learning --

How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before introducing the open source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.--

ISBN: 9781491914250UK39.99

LCCN: 2017277169Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: QA325.5 / .P38 2017

Dewey Class. No.: 006.3/1

Deep learning : = a practitioner's approach /
LDR:02019nam a2200217 a 4500 001 2109479
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010 $a 2017277169
020 $a 9781491914250 $q (pbk.) : $c UK39.99
020 $a 1491914254 $q (pbk.)
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050 0 0 $a QA325.5 $b .P38 2017
082 0 4 $a 006.3/1 $2 23
100 1 $a Patterson, Josh. $3 3272057
245 1 0 $a Deep learning : $b a practitioner's approach / $c Josh Patterson and Adam Gibson.
250 $a 1st ed.
260 # $a Sebastopol, CA : $b O'Reilly, $c 2017.
300 $a xxi, 507 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and index.
505 0 # $t A review of machine learning -- $t Foundations of neural networks and deep learning -- $t Fundamentals of deep networks -- $t Major architecture of deep networks -- $t Building deep networks -- $t Tuning deep networks -- $t Tuning specific deep network architectures -- $t Vectorization -- $t Using deep learning and DL4J on Spark -- $t What is artificial intelligence? -- $t RL4J and reinforcement learning -- $t Numbers everyone should know -- $t Neural networks and backpropagation : a mathematical approach -- $t Using the ND4J API -- $t Using DataVec -- $t Working with DL4J from source -- $t Setting up DL4J projects -- $t Setting up GPUs for DL4J projects -- $t Troubleshooting DL4J installations.
520 # $a How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before introducing the open source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you'll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.-- $c Source other than Library of Congress.
650 # 0 $a Machine learning. $3 533906
650 # 0 $a Neural networks (Computer science) $3 532070
650 # 0 $a Open source software. $3 581998
700 1 # $a Gibson, Adam. $3 3272058