ExploringanadvancedstateoftheartdeeplearningmodelsanditsapplicationsusingPopularpythonlibrarieslikeKeras,Tensorflow,andPytorchKeyFeatures•AstrongfoundationonneuralnetworksanddeeplearningwithPythonlibraries.•ExploreadvanceddeeplearningtechniquesandtheirapplicationsacrosscomputervisionandNLP.•Learnhowacomputercannavigateincomplexenvironmentswithreinforcementlearning.BookDescriptionWiththesurgeofArtificialIntelligenceineachandeveryapplicationcateringtobothbusinessandconsumerneeds,DeepLearningbecomestheprimeneedoftodayandfuturemarketdemands.Thisbookexploresdeeplearningandbuildsastrongdeeplearningmindsetinordertoputthemintouseintheirsmartartificialintelligenceprojects.Thissecondeditionbuildsstronggroundsofdeeplearning,deepneuralnetworksandhowtotrainthemwithhigh-performancealgorithmsandpopularpythonframeworks.Youwilluncoverdifferentneuralnetworksarchitectureslikeconvolutionalnetworks,recurrentnetworks,longshortter妹妹emory(LSTM)andsolveproblemsacrossimagerecognition,naturallanguageprocessing,andtime-seriesprediction.Youwillalsoexplorethenewlyevolvedareaofreinforcementlearninganditwillhelpyoutounderstandthestate-of-the-artalgorithmswhicharethemainenginesbehindpopulargameGo,Atari,andDota.Bytheendofthebook,youwillbewellversedwithpracticaldeeplearningknowledgeanditsreal-worldapplicationsWhatyouwilllearn•Graspmathematicaltheorybehindneuralnetworksanddeeplearningprocess.•Investigateandresolvecomputervisionchallengesusingconvolutionalnetworksandcapsulenetworks.•SolveGenerativetasksusingVariationalAutoencodersandGenerativeAdversarialNets(GANs).•ExploreReinforcementLearningandunderstandhowagentsbehaveinacomplexenvironment.•Implementcomplexnaturallanguageprocessingtasksusingrecurrentnetworks(LSTM

NaturalLanguageProcessingwithPyTorch:BuildIntelligentLanguageApplicationsUsingDeepLearningBy作者:DelipRao–BrianMcMahanISBN-10书号:1491978236ISBN-13书号:9781491978238Edition版本:1出书日期:2019-02-11pages页数:(256)$89.99NaturalLanguageProcessing(NLP)providesboundlessopportunitiesforsolvingproblemsinartificialintelligence,makingproductssuchasAmazonAlexaandGoogleTranslatepossible.Ifyou’readeveloperordatascientistnewtoNLPanddeeplearning,thispracticalguideshowsyouhowtoapplythesemethodsusingPyTorch,aPython-baseddeeplearninglibrary.AuthorsDelipRaoandBrianMcMahonprovideyouwithasolidgroundinginNLPanddeeplearningalgorithmsanddemonstratehowtousePyTorchtobuildapplicationsinvolvingrichrepresentationsoftextspecifictotheproblemsyouface.Eachchapterincludesseveralcodeexamplesandillustrations.ExplorecomputationalgraphsandthesupervisedlearningparadigmMasterthebasicsofthePyTorchoptimizedtensormanipulationlibraryGetanoverviewoftraditionalNLPconceptsandmethodsLearnthebasicideasinvolvedinbuildingneuralnetworksUseembeddingstorepresentwords,sentences,documents,andotherfeaturesExploresequencepredictionandgeneratesequence-to-sequencemodelsLearndesignpatternsforbuildingproductionNLPsystems

DavidSilver强化学习课程文件Lecture1:IntroductiontoReinforcementLearningLecture2:MarkovDecisionProcessesLecture3:PlanningbyDynamicProgra妹妹ingLecture4:Model-FreePredictionLecture5:Model-FreeControlLecture6:ValueFunctionApproximationLecture7:PolicyGradientMethodsLecture8:IntegratingLearningandPlanningLecture9:ExplorationandExploitationLecture10:CaseStudy:RLinClassicGames

Content1.回顾deeplearning在图像上的经典应用1.1Autoencoder1.2MLP1.3CNN2.deeplearning处理语音等时序信号2.1对什么时序信号处理什么问题2.2准备知识2.2.1HiddenMarkovModel(HMM)2.2.2GMM-HMMforSpeechRecognition2.2.3RestrictedBoltzmannMachine（RBM）3.DBN和RNN在语音上的应用3.1DBN3.1.1DBN架构3.1.2DBN-DNNforSpeechRecognition3.2RNN3.2.1RNN种类3.2.2RNN-RBMforSequentialsignalPrediction

Loan_prediction:在DataHack上做预尝试-AnalyticsVidhya

ThereareamultitudeofbooksouttheretohelpyouhoneyourVerificationskills,asalmosteveryDigitalICprofessionalwilltellyou.Theavailablebooksrunthegamutofqualityandeaseofuse.Themajorstrengthofthisbookisaplacewheremanyofthosebooksfalldown–theyarenotwhattheyclaimtobe.Vanessa’sbookisagreatexampleofexactlywhatitclaimstobe–aguideforthe“beginner”.“Beginner”isusedlooselyinthiscontextbecausethebookdoesnotshyawayfromadvancedconcepts(theFactory,registerpackageprediction,etc.)

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WEPP模型（WaterErosionPredictionProject）是美国农业部开发的水蚀预测软件，次要用来预测耕地、草地和林地中的土壤水蚀、暴雨径流、根系层土壤水分、蒸散作用、植物生长以及积雪的融化等。
WEPP模型还可以用以评价各种流域的管理活动。
WEPP根据基本的入渗理论、渗漏、土壤物理特性、植物学、水利学和侵蚀机制提出了一种新的侵蚀预测技术。
作为水文模型，WEPP具有以下一些优点：首先，它反映了农、林、牧土地利用的效益，其次是可以描述能影响单个坡面和整个小流域地表和地下水数量、质量的因素在时间和空间上的变化。

SVM的数据分类预测—意大利葡萄酒品种识别的matlab源程序与数据-SVMpredictiondataclassification-ItalianWinetyperecognitionmatlabsourcecodeanddata

哈佛大学能耗预测项目（PredictionofBuildingsEnergyConsumption）完好的机器学习项目代码+分析过程

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