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.

专家零碎之算法设计Mathematics--ArtificialIntelligenceAndExpertSystems

Firstrelease:26October2017www.sciencemag.org(Pagenumbersnotfinalattimeoffirstrelease)1Theabilitytolearnandgeneralizefromafewexamplesisahallmarkofhumanintelligence(1).CAPTCHAs,imagesusedbywebsitestoblockautomatedinteractions,areexamplesofproblemsthatareeasyforhumansbutdifficultforcomput-ers.CAPTCHAsarehardforalgorithmsbecausetheyaddclutterandcrowdletterstogethertocreateachicken-and-eggproblemforcharacterclassifiers—theclassifiersworkwellforcharactersthathavebeensegmentedout,butsegmentingtheindividualcharactersrequiresanunderstandingofthecharacters,eachofwhichmightberenderedinacombinato-rialnumberofways(2–5).Arecentdeep-learningapproachforparsingonespecificCAPTCHAstylerequiredmillionsoflabeledexamplesfromit(6),andearlierapproachesmostlyreliedonhand-craftedstyle-specificheuristicstosegmentoutthecharacter(3,7);whereashumanscansolvenewstyleswithoutexplicittraining(Fig.1A).Thewidevarietyofwaysinwhichletterformscouldberenderedandstillbeunder-stoodbypeopleisillustratedinFig.1.Buildingmodelsthatgeneralizewellbeyondtheirtrain-ingdistributionisanimportantsteptowardtheflexibilityDouglasHofstadterenvisionedwhenhesaidthat“foranyprogramtohandleletterformswiththeflexibilitythathumanbeingsdo,itwouldhavetopossessfull-scaleartificialintelli-gence”(8).Manyresearchershaveconjecturedthatthiscouldbeachievedbyincorporatingtheinductivebiasesofthevis-ualcortex(9–12),utilizingthewealthofdatageneratedbyneuroscienceandcognitivescienceresearch.Inthema妹妹a-lianbrain,feedbackconnectionsinthevisualcortexplayrolesinfigure-ground-segmentation,andinobject-basedtop-downattentionthatisolatesthecontoursofanobjectevenwhenpartiallytransparentobjectsoccupythesamespatiallocations(13–16).Lateralconnectionsinthevisualco

Hands-OnComputerVisionwithJulia:BuildcomplexapplicationswithadvancedJuliapackagesforimageprocessing,neuralnetworks,andArtificialIntelligenceExplorethevariouspackagesinJuliathatsupportimageprocessingandbuildneuralnetworksforvideoprocessingandobjecttracking.KeyFeaturesBuildafull-fledgedimageprocessingapplicationusingJuliaImagesPerformbasictoadvancedimageandvideostreamprocessingwithJulia'sAPIsUnderstandandoptimizevariousfeaturesofOpenCVwitheasyexamplesBookDescriptionHands-OnComputerVisionwithJuliaisathoroughguidefordeveloperswhowanttogetstartedwithbuildingcomputervisionapplicationsusingJulia.Juliaiswellsuitedtoimageprocessingbecauseit'seasytouseandletsyouwriteeasy-to-compileandefficientmachinecode.ThisbookbeginsbyintroducingyoutoJulia'simageprocessinglibrariessuchasImages.jlandImageCore.jl.You'llgettogripswithanalyzingandtransformingimagesusingJuliaImages;someofthetechniquesdiscussedincludeenhancingandadjustingimages.Asyoumakeyourwaythroughthechapters,you'lllearnhowtoclassifyimages,clusterthem,andapplyneuralnetworkstosolvecomputervisionproblems.Intheconcludingchapters,youwillexploreOpenCVapplicationstoperformreal-timecomputervisionanalysis,forexample,facedetectionandobjecttracking.YouwillalsounderstandJulia'sinteractionwithTesseracttoperformopticalcharacterrecognitionandbuildanapplicationthatbringstogetherallthetechniquesweintroducedpreviouslytoconsolidatetheconceptslearned.Byendofthebook,youwillhaveunderstoodhowtoutilizevariousJuliapackagesandafewopensourcelibrariessuchasTesseractandOpenCVtosolvecomputervisionproblemswithease.WhatyouwilllearnAnalyzeimagemetadataandidentifycriticaldatausingJuliaImagesApplyfiltersandimproveimagequalityandcolorschemesExtract2Dfeaturesforimagecom

ThishighlyaccessibleintroductiontoLispissuitablebothfornovicesapproachingtheirfirstprogra妹妹inglanguageandexperiencedprogra妹妹ersinterestedinexploringakeytoolforartificialintelligenceresearch.Thetextoffersclear,reader-friendlyexplanationsofsuchessentialconceptsasconscellstructures,evaluationrules,programsasdata,andrecursiveandapplicativeprogra妹妹ingstyles.Thetreatmentincorporatesseveralinnovativeinstructionaldevices,suchastheuseoffunctionboxesinthefirsttwochapterstovisuallydistinguishfunctionsfromdata,useofevaltracenotationinlaterchapterstoillustratetheoperationofevaluationrules,and"Dragonstories"toexplainrecursion.Thebookcontainsnearly400diagramsandillustrations,and77pagesofanswerstoexercises.Advancedtopicsand"toolkit"sections,andavarietyofcompleteprograms,extendreaders'progra妹妹ingpower.

内含pdf和epub两种格局电子书籍MATLABDeepLearning：WithMachineLearning,NeuralNetworksandArtificialIntelligence

很好的学习资料，中文英文都有，可以根据本人的情况学习

Forthoseenteringthefieldofartificialneuralnetworks,therehasbeenanacuteneedforanauthoritativetextbookthatexplainsthemainideasclearlyandconsistentlyusingthebasictoolsoflinearalgebra,calculus,andsimpleprobabilitytheory.Therehavebeenmanyattemptstoprovidesuchatext,butuntilnow,nonehassucceeded.Someauthorshavefailedtoseparatethebasicideasandprinciplesfromthesoftandfuzzyintuitionsthatledtosomeofthemodelsaswellastomostoftheexaggeratedclaims.Othershavebeenunwillingtousethebasicmathematicaltoolsthatareessentialforarigorousunderstandingofthematerial.Yetothershavetriedtocovertoomanydifferentkindsofneuralnetworkwithoutgoingintoenoughdepthonanyoneofthem.Themostsuccessfulattempttodatehasbeen"IntroductiontotheTheoryofNeuralComputation"byHertz,KroghandPalmer.Unfortunately,thisbookstartedlifeasagraduatecourseinstatisticalphysicsanditshows.Sodespiteitsmanyadmirablequalitiesitisnotidealasageneraltextbook.

ArtificialIntelligenceforHumans,Volume1-2，包括AI基本算法，自然规律启示的算法以及深度学习

Deeplearninginneuralnetworks:AnoverviewInrecentyears,deepartificialneuralnetworks(includingrecurrentones)havewonnumerouscontestsinpatternrecognitionandmachinelearning.Thishistoricalsurveycompactlysummarizesrelevantwork,muchofitfromthepreviousmillennium.ShallowandDeepLearnersaredistinguishedbythedepthoftheircreditassignmentpaths,whicharechainsofpossiblylearnable,causallinksbetweenactionsandeffects.Ireviewdeepsupervisedlearning(alsorecapitulatingthehistoryofbackpropagation),unsupervisedlearning,reinforcementlearning&evolutionarycomputation,andindirectsearchforshortprogramsencodingdeepandlargenetworks.

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。