高雄醫學大學圖書館 |

Language: English

Back

Deep learning :a practitioner's approach /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning :/ Josh Patterson and Adam Gibson.
Reminder of title:	a practitioner's approach /
Author:	Patterson, Josh
other author:	Gibson, Adam.
Published:	Sebastopol, CA :O'Reilly, : c2017.,
[NT 15000922]:	1st ed.
Description:	xxi, 507 p. :ill. ; : 24 cm.;
基督教聖經之智慧書導讀 :	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.--
Subject:	Machine learning. -
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:

248498
Machine learning.

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

Dewey Class. No.: 006.3/1

Deep learning :a practitioner's approach /
LDR:02088cam 2200253 a 4500 001 323837
005 20200130143129.0
008 200215s2017 caua b 001 0 eng d
010 $a 2017277169
020 $a 9781491914250 (pbk.) : $c NT1811
020 $a 1491914254 (pbk.)
035 $a (OCoLC)ocn902657832
035 $a 00380240
040 $a KMU $d KMU
042 $a lccopycat
050 0 0 $a QA325.5 $b .P38 2017
082 0 4 $a 006.3/1 $2 23
087 $a 312.9831 $b P317 2017
100 1 $a Patterson, Josh $c (Consultant) $3 447694
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 c2017.
300 $a xxi, 507 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and index.
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.
650 0 $2 96060 $a Machine learning. $3 248498
650 0 $2 96060 $a Neural networks (Computer science) $3 233635
650 0 $a Open source software. $3 319977
700 1 $a Gibson, Adam. $3 447695