MARC Record from marc_columbia

LEADER: 04041cam a2200673Ii 4500
001 13668444
005 20220514225813.0
006 m o d
007 cr cnu|||unuuu
008 170801s2017 caua o 001 0 eng d
035 $a(OCoLC)ocn997423738
035 $a(NNC)13668444
040 $aN$T$beng$erda$epn$cN$T$dIDEBK$dEBLCP$dTEFOD$dYDX$dTEFOD$dOCLCQ$dN$T$dUMI$dMERER$dOCLCF$dOCLCQ$dTOH$dSTF$dW2U$dOCLCQ$dVT2$dALAUL$dNRC$dOCLCQ$dHCO$dUOK$dCEF$dKSU$dOCLCQ$dU3W$dWYU$dOCLCQ$dC6I$dZCU$dUAB$dUKAHL$dRDF$dOCLCQ$dOCLCO
019 $a999544158$a1000155572$a1002905278
020 $a9781491914236$q(electronic bk.)
020 $a1491914238$q(electronic bk.)
020 $a9781491914212$q(electronic bk.)
020 $a1491914211$q(electronic bk.)
020 $z9781491914250
020 $z1491914254
020 $z9781491924570
035 $a(OCoLC)997423738$z(OCoLC)999544158$z(OCoLC)1000155572$z(OCoLC)1002905278
037 $aAD209375-0802-482F-ABA4-D5EFC47F1E2C$bOverDrive, Inc.$nhttp://www.overdrive.com
050 4 $aQA325.5$b.P38 2017eb
072 7 $aCOM$x000000$2bisacsh
082 04 $a006.31$223
049 $aZCUA
100 1 $aPatterson, Josh,$eauthor.
245 10 $aDeep learning :$ba practitioner's approach /$cJosh Patterson and Adam Gibson.
250 $aFirst edition.
264 1 $aSebastopol, CA :$bO'Reilly Media, Inc.,$c2017.
264 4 $c©2017
300 $a1 online resource (507 pages) :$bcolor illustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
500 $aIncludes index.
588 0 $aOnline resource; title from PDF title page (EBSCO, viewed August 24, 2017).
505 0 $aA 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 $aHow 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.
650 0 $aMachine learning.
650 0 $aArtificial intelligence.
650 0 $aNeural networks (Computer science)
650 2 $aArtificial Intelligence
650 2 $aNeural Networks, Computer
650 6 $aApprentissage automatique.
650 6 $aIntelligence artificielle.
650 6 $aRéseaux neuronaux (Informatique)
650 7 $aartificial intelligence.$2aat
650 7 $aCOMPUTERS$xGeneral.$2bisacsh
650 7 $aArtificial intelligence.$2fast$0(OCoLC)fst00817247
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
650 7 $aNeural networks (Computer science)$2fast$0(OCoLC)fst01036260
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aGibson, Adam,$eauthor.
776 08 $iPrint version:$aPatterson, Josh.$tDeep learning.$bFirst edition.$d©2017$z1491914254$z9781491914250$w(OCoLC)902657832
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio13668444$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS