KannalaJ,BrandtSS.Agenericcameramodelandcalibrationmethodforconventional,wide-angle,andfish-eyelenses[J].PatternAnalysisandMachineIntelligence,IEEETransactionson,2006,28(8):1335-1340

Totally56benchmarkfunctionsfortesting,theyareusedforintelligencealgorithmtesting.

LearnhowtomodelandtrainadvancedneuralnetworkstoimplementavarietyofComputerVisiontasksKeyFeaturesTraindifferentkindsofdeeplearningmodelfromscratchtosolvespecificproblemsinComputerVisionCombinethepowerofPython,Keras,andTensorFlowtobuilddeeplearningmodelsforobjectdetection,imageclassification,similaritylearning,imagecaptioning,andmoreIncludestipsonoptimizingandimprovingtheperformanceofyourmodelsundervariousconstraintsBookDescriptionDeeplearninghasshownitspowerinseveralapplicationareasofArtificialIntelligence,especiallyinComputerVision.ComputerVisionisthescienceofunderstandingandmanipulatingimages,andfindsenormousapplicationsintheareasofrobotics,automation,andsoon.Thisbookwillalsoshowyou,withpracticalexamples,howtodevelopComputerVisionapplicationsbyleveragingthepowerofdeeplearning.Inthisbook,youwilllearndifferenttechniquesrelatedtoobjectclassification,objectdetection,imagesegmentation,captioning,imagegeneration,faceanalysis,andmore.YouwillalsoexploretheirapplicationsusingpopularPythonlibrariessuchasTensorFlowandKeras.Thisbookwillhelpyoumasterstate-of-the-art,deeplearningalgorithmsandtheirimplementation.WhatyouwilllearnSetupanenvironmentfordeeplearningwithPython,TensorFlow,andKerasDefineandtrainamodelforimageandvideoclassificationUsefeaturesfromapre-trainedConvolutionalNeuralNetworkmodelforimageretrievalUnderstandandimplementobjectdetectionusingthereal-worldPedestrianDetectionscenarioLearnaboutvariousproblemsinimagecaptioningandhowtoovercomethembytrainingimagesandtexttogetherImplementsimilaritymatchingandtrainamodelforfacerecognitionUnderstandtheconceptofgenerativemodelsandusethemforimagegenerationDeployyourdeeplearningmodelsandoptimizethemforhighperformanceWhoThisBookIsForThisbookistargeted

人工智能（ArtificialIntelligence），英文缩写为AI。
它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。
人工智能是计算机科学的一个分支，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器，该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。
人工智能从诞生以来，理论和技术日益成熟，应用领域也不断扩大，可以设想，未来人工智能带来的科技产品，将会是人类智慧的“容器”，也可能超过人的智能。

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

Geneticprogra妹妹ing,geneticalgorithms,human-competitivemachineintelligence,machinelearning,schematheory

ThisbookisintendedforintermediatetoadvancedC++gamedeveloperswhoarelookingtotaketheirskillstothenextlevelandlearnthedeepconceptsof3Dgamedevelopment.ThereaderwilllearnthekeyconceptsusedinthedevelopmentofAAA-levelgames.Advancedtopics,suchaslibrarycreation,artificialintelligence,shadertechniques,advancedeffectsandlighting,toolcreation,physics,networking,andothercriticalgamesystems,willbecoveredthroughoutthejourney.

MachineLearningprojectsourultimatedesiretounderstandtheessenceofhumanintelligenceontothespaceoftechnology.Assuch,whileitcannotbefullyunderstoodintherestrictedfieldofcomputerscience,itisnotnecessarilythesearchofcleveremulationsofhumancognition.Whilediggin

Neuralnetworkshavemadeasurprisecomebackinthelastfewyearsandhavebroughttremendousinnovationintheworldofartificialintelligence.ThegoalofthisbookistoprovideC#progra妹妹erswithpracticalguidanceinsolvingcomplexcomputationalchallengesusingneuralnetworksandC#librariessuchasCNTK,andTensorFlowSharp.Thisbookwilltakeyouonastep-by-steppracticaljourney,coveringeverythingfromthemathematicalandtheoreticalaspectsofneuralnetworks,tobuildingyourowndeepneuralnetworksintoyourapplicationswiththeC#and.NETframeworks.Thisbookbeginsbygivingyouaquickrefresherofneuralnetworks.YouwilllearnhowtobuildaneuralnetworkfromscratchusingpackagessuchasEncog,Aforge,andAccord.Youwilllearnaboutvariousconceptsandtechniques,suchasdeepnetworks,perceptrons,optimizationalgorithms,convolutionalnetworks,andautoencoders.Youwilllearnwaystoaddintelligentfeaturestoyour.NETapps,suchasfacialandmotiondetection,objectdetectionandlabeling,languageunderstanding,knowledge,andintelligentsearch.Throughoutthisbook,youwillbeworkingoninterestingdemonstrationsthatwillmakeiteasiertoimplementcomplexneuralnetworksinyourenterpriseapplications.

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。