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首页Deep Learning with Hadoop
This book will teach you how to deploy large-scale datasets in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning.
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TableofContents
DeepLearningwithHadoop
Credits
AbouttheAuthor
AbouttheReviewers
www.PacktPub.com
Whysubscribe?
CustomerFeedback
Dedication
Preface
Whatthisbookcovers
Whatyouneedforthisbook
Whothisbookisfor
Conventions
Readerfeedback
Customersupport
Downloadingtheexamplecode
Downloadingthecolorimagesofthisbook
Errata
Piracy
Questions
1.IntroductiontoDeepLearning
Gettingstartedwithdeeplearning
Deepfeed-forwardnetworks
Variouslearningalgorithms
Unsupervisedlearning
Supervisedlearning
Semi-supervisedlearning
Deeplearningterminologies
Deeplearning:ArevolutioninArtificialIntelligence
Motivationsfordeeplearning
Thecurseofdimensionality
Thevanishinggradientproblem
Distributedrepresentation
Classificationofdeeplearningnetworks
Deepgenerativeorunsupervisedmodels
Deepdiscriminatemodels
Summary
2.DistributedDeepLearningforLarge-ScaleData
Deeplearningformassiveamountsofdata
Challengesofdeeplearningforbigdata
Challengesofdeeplearningduetomassivevolumesofdata(firstV)
Challengesofdeeplearningfromahighvarietyofdata(secondV)
Challengesofdeeplearningfromahighvelocityofdata(thirdV)

Challengesofdeeplearningtomaintaintheveracityofdata(fourthV)
DistributeddeeplearningandHadoop
Map-Reduce
IterativeMap-Reduce
YetAnotherResourceNegotiator(YARN)
Importantcharacteristicsfordistributeddeeplearningdesign
Deeplearning4j-anopensourcedistributedframeworkfordeeplearning
MajorfeaturesofDeeplearning4j
SummaryoffunctionalitiesofDeeplearning4j
SettingupDeeplearning4jonHadoopYARN
GettingfamiliarwithDeeplearning4j
IntegrationofHadoopYARNandSparkfordistributeddeeplearning
RulestoconfigurememoryallocationforSparkonHadoopYARN
Summary
3.ConvolutionalNeuralNetwork
Understandingconvolution
BackgroundofaCNN
Architectureoverview
BasiclayersofCNN
ImportanceofdepthinaCNN
Convolutionallayer
Sparseconnectivity
Improvedtimecomplexity
Parametersharing
Improvedspacecomplexity
Equivariantrepresentations
ChoosingthehyperparametersforConvolutionallayers
Depth
Stride
Zero-padding
Mathematicalformulationofhyperparameters
Effectofzero-padding
ReLU(RectifiedLinearUnits)layers
AdvantagesofReLUoverthesigmoidfunction
Poolinglayer
Whereisituseful,andwhereisitnot?
Fullyconnectedlayer
DistributeddeepCNN
Mostpopularaggressivedeepneuralnetworksandtheirconfigurations
Trainingtime-majorchallengesassociatedwithdeepneuralnetworks
HadoopfordeepCNNs
ConvolutionallayerusingDeeplearning4j
Loadingdata
Modelconfiguration
Trainingandevaluation
Summary
4.RecurrentNeuralNetwork

Whatmakesrecurrentnetworksdistinctivefromothers?
Recurrentneuralnetworks(RNNs)
Unfoldingrecurrentcomputations
Advantagesofamodelunfoldedintime
MemoryofRNNs
Architecture
Backpropagationthroughtime(BPTT)
Errorcomputation
Longshort-termmemory
Problemwithdeepbackpropagationwithtime
Longshort-termmemory
Bi-directionalRNNs
ShortfallsofRNNs
Solutionstoovercome
DistributeddeepRNNs
RNNswithDeeplearning4j
Summary
5.RestrictedBoltzmannMachines
Energy-basedmodels
Boltzmannmachines
HowBoltzmannmachineslearn
Shortfall
RestrictedBoltzmannmachine
Thebasicarchitecture
HowRBMswork
ConvolutionalRestrictedBoltzmannmachines
StackedConvolutionalRestrictedBoltzmannmachines
DeepBeliefnetworks
Greedylayer-wisetraining
DistributedDeepBeliefnetwork
DistributedtrainingofRestrictedBoltzmannmachines
DistributedtrainingofDeepBeliefnetworks
Distributedbackpropagationalgorithm
PerformanceevaluationofRBMsandDBNs
Drasticimprovementintrainingtime
ImplementationusingDeeplearning4j
RestrictedBoltzmannmachines
DeepBeliefnetworks
Summary
6.Autoencoders
Autoencoder
Regularizedautoencoders
Sparseautoencoders
Sparsecoding
Sparseautoencoders
Thek-Sparseautoencoder
Howtoselectthesparsitylevelk

Effectofsparsitylevel
Deepautoencoders
Trainingofdeepautoencoders
ImplementationofdeepautoencodersusingDeeplearning4j
Denoisingautoencoder
ArchitectureofaDenoisingautoencoder
Stackeddenoisingautoencoders
ImplementationofastackeddenoisingautoencoderusingDeeplearning4j
Applicationsofautoencoders
Summary
7.MiscellaneousDeepLearningOperationsusingHadoop
DistributedvideodecodinginHadoop
Large-scaleimageprocessingusingHadoop
ApplicationofMap-Reducejobs
NaturallanguageprocessingusingHadoop
Webcrawler
Extractionofkeywordandmodulefornaturallanguageprocessing
Estimationofrelevantkeywordsfromapage
Summary
1.References
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