ICCV2017相关论文下载，文章共9篇，包含CrossView方法、Unsupervised方法、改进的TripletLoss方法等。

Deeplearningsimplifiedbytakingsupervised,unsupervised,andreinforcementlearningtothenextlevelusingthePythonecosystemTransferlearningisamachinelearning(ML)techniquewhereknowledgegainedduringtrainingasetofproblemscanbeusedtosolveothersimilarproblems.Thepurposeofthisbookistwo-fold;firstly,wefocusondetailedcoverageofdeeplearning(DL)andtransferlearning,comparingandcontrastingthetwowitheasy-to-followconceptsandexamples.Thesecondareaoffocusisreal-worldexamplesandresearchproblemsusingTensorFlow,Keras,andthePythonecosystemwithhands-onexamples.ThebookstartswiththekeyessentialconceptsofMLandDL,followedbydepictionandcoverageofimportantDLarchitecturessuchasconvolutionalneuralnetworks(CNNs),deepneuralnetworks(DNNs),recurrentneuralnetworks(RNNs),longshort-termmemory(LSTM),andcapsulenetworks.Ourfocusthenshiftstotransferlearningconcepts,suchasmodelfreezing,fine-tuning,pre-trainedmodelsincludingVGG,inception,ResNet,andhowthesesystemsperformbetterthanDLmodelswithpracticalexamples.Intheconcludingchapters,wewillfocusonamultitudeofreal-worldcasestudiesandproblemsassociatedwithareassuchascomputervision,audioanalysisandnaturallanguageprocessing(NLP).Bytheendofthisbook,youwillbeabletoimplementbothDLandtransferlearningprinciplesinyourownsystems.WhatyouwilllearnSetupyourownDLenvironmentwithgraphicsprocessingunit(GPU)andCloudsupportDelveintotransferlearningprincipleswithMLandDLmodelsExplorevariousDLarchitectures,includingCNN,LSTM,andcapsulenetworksLearnaboutdataandnetworkrepresentationandlossfunctionsGettogripswithmodelsandstrategiesintransferlearningWalkthroughpotentialchallengesinbuildingcomplextransferlearningmodelsfromscratchExplorereal-worldresearchproblemsrelatedtocompute

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