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Deep learning :a practitioner's appr...

Patterson, Josh ((Consultant))

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, : c2017.,
版本:	1st ed.
面頁冊數:	xxi, 507 p. :ill. ; : 24 cm.;
提要註:	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.--
標題:	Open source software. -
ISBN:	9781491914250 (pbk.) :

Deep learning :a practitioner's approach /
Patterson, Josh(Consultant)

Deep learning :a practitioner's approach /Josh Patterson and Adam Gibson. - 1st ed. - Sebastopol, CA :O'Reilly,c2017. - xxi, 507 p. :ill. ;24 cm.

Includes bibliographical references and index.

A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architecture of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on Spark -- What is artificial intelligence? -- RL4J and reinforcement learning -- Numbers everyone should know -- Neural networks and backpropagation: a mathematical approach -- Using the ND4J API -- Using DataVec -- Working with DL4J from source -- Setting up DL4J projects -- Setting up GPUs for DL4J projects -- Troubleshooting DL4J installations.

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: 9781491914250 (pbk.) :NT1811

LCCN: 2017277169Subjects--Topical Terms:

319977
Open source software.

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

Dewey Class. No.: 006.3/1

Deep learning :a practitioner's approach /
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505 0 $a A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architecture of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on Spark -- What is artificial intelligence? -- RL4J and reinforcement learning -- Numbers everyone should know -- Neural networks and backpropagation: a mathematical approach -- Using the ND4J API -- Using DataVec -- Working with DL4J from source -- Setting up DL4J projects -- Setting up GPUs for DL4J projects -- 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.
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