WelcometoLongShort-TermMemoryNetworksWithPython.LongShort-TermMemory(LSTM)recurrentneuralnetworksareoneofthemostinterestingtypesofdeeplearningatthemoment.Theyhavebeenusedtodemonstrateworld-classresultsincomplexproblemdomainssuchaslanguagetranslation,automaticimagecaptioning,andtextgeneration.LSTMsareverydi↵erenttootherdeeplearningtechniques,suchasMultilayerPerceptrons(MLPs)andConvolutionalNeuralNetworks(CNNs),inthattheyaredesignedspecificallyforsequencepredictionproblems.IdesignedthisbookforyoutorapidlydiscoverwhatLSTMsare,howtheywork,andhowyoucanbringthisimportanttechnologytoyourownsequencepredictionproblems.

Deeplearningallowscomputationalmodelsthatarecomposedofmultipleprocessinglayerstolearnrepresentationsofdatawithmultiplelevelsofabstraction.Thesemethodshavedramaticallyimprovedthestate-of-the-artinspeechrecognition,visualobjectrecognition,objectdetectionandmanyotherdomainssuchasdrugdiscoveryandgenomics.Deeplearningdiscoversintricatestructureinlargedatasetsbyusingthebackpropagationalgorithmtoindicatehowamachineshouldchangeitsinternalparametersthatareusedtocomputetherepresentationineachlayerfromtherepresentationinthepreviouslayer.Deepconvolutionalnetshavebroughtaboutbreakthroughsinprocessingimages,video,speechandaudio,whereasrecurrentnetshaveshonelightonsequentialdatasuchastextandspeech.

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

leetcodepython题解，包含大量leetcode题目的解法，源代码，python实现CourseSchedule21.4.4Numberofislands14.5HeapsMergeKSortedLinkedLists1.5.1KthLargestElementinanArray1.5.2Arrays1.62sum‖l1.62SumⅢ1.6.2ContainsDuplicate1.6.3RotateArray1.643SumSmaller1.653Sumclosest1.663Sum1.6.7TwoSum1.68PlusOne1.6.9BestTimetoBuyandSellStock1.6.10Shortestworddistance1.6.11Movezeroes1.6.12ContainsDuplicate1.6.13MajorityElement1.6.14RemoveDuplicatesfromSortedArray1.6.15NestedListWeightSum1.6.16NestedListWeightedSumIl1.6.17Removeelement1.6.18IntersectionofTwoArraysll1.6.19MergeSortedArrays1.6.20ReverseVowelsofaString1.6.21IntersectionofTwoArrays1.6.22Containerwithmostwater1.6.23ProductofArrayExceptSelf1.6.24TrappingRainWater1.6.25MaximumSubarray1.6.26BestTimetoBuyandSellStockIl1.6.27FindMinimuminRotatedSortedArray1.6.28Pascal'sTriangle1.6.29Pascal'sTriangle‖l1.6.30SummaryRanges1.6.31MissingNumber1.6.32StringsValidAnagram1.7.1Validpalindrome1.7.2WordPattern1.7.3ValidParentheses1.7.4IsomorphicStrings1.7.5ReverseString1.7.6BitManipulationSumofTwoIntegers18.1SingleNumber18.2Singlenumber‖18.3SingleNumberIll1.8.4Maths1.9ReverseInteger1.9.1Palindromenumber19.2Pow(x,n)19.3Subsets1.94Subsets‖195FractiontoRecurringDecimal19.6Excelsheetcolumnnumber19.7Excelsheetcolumntitle19.8FactorialTrailingzeros199HappyNumber1.9.10Countprimes1.9.11Plusone19.12DivideTwoIntegers19.13MultiplyStrings1.9.14MaxPointsonaline1.9.15ProductofArrayExceptSelf19.16Powerofthree19.17IntegerBreak1.9.18Poweroffour9.19Adddigits1.9.20UglyNumber1.9.21glyNumberll1.9.22SuperUglyNumber19.23FindKpairswithsmallestsums1.924SelfCrossing1.9.25Paintfence1.9.26Bulbswitcher19.27Nimgame1.9.28Matrix1.10RotateImage1.10.1SetmatrixZeroes1.10.2Searcha2DMatrix1.10.3Searcha2dMatrixl1.10.4SpiralMatrix1.10.5SpiralMatrix‖l1.10.6DesignLRUCache1.11.1IntroductionMyLeetcodeSolutionsinPythonThisbookwillcontainmysolutionsinPythontotheleetcodeproblems.Currently,willjusttrytoposttheacceptedsolutions.TheplanistoeventuallyincludedetailedexplanationsofeachandeverysolutionamdoingthisjustforfunLinkedListCycleLinkedListCvcleGivenalinkedlist,determineifithasacycleinitFollowup:Canyousolveitwithoutusingextraspace?Url:https://leetcode.com/problems/linked-list-cycle/Definitionforsingly-linkedlistclassListNodeobject)###definit(self,x)self,val=xself,nextNoneclassSolution(object):defhasCycle(self,head)IItypehead:ListNodertype:boolIIIIifhead=nonereturnfalseelsefastheadslow=headWhilefastnoneandfast.nextnonesloW=slownextfastfast.nextnextiffast=slow:breaki千fastNoneorfast.next=nonereturnFalseeliffast=slowreturntruereturnfalseLinkedListCycleReverseLinkedListReverseLinkedlistReverseasinglylinkedlistUrl:https://eetcode.com/problems/reverse-linked-list/definitionforsingly-linkedlist#tclassListNode(object):##def-init(self,x)self.∨al=xselfnextnoneclassSolution(object):defreverseList(self,head)11IIl1typehead:ListNodertype:ListNodeifhead=nonereturnnoneelifhead!=noneandheadnext=nonereturnheadelsetempNonenextnodenoneWhileheadNonenextnodeheadnexthead.nexttemptemp=headheadnextnodereturntempDeletenodeinalinkedlistDeletenodeinalinkedlistWriteafunctiontodeleteanode(exceptthetail)inasinglylinkedlist,givenonlyaccesstothatnodeSupposedthelinkedlistis1->2->3->4andyouaregiventhethirdnodewithvalue3,thelinkedlistshouldbecome1->2->4aftercallingyourfunctionUrl:https://eetcode.com/problems/delete-node-in-a-linked-list/Definitionforsingly-linkedlistclassListNode(object):#def-init(self,x)#self,valxself,nextNoneclasssolution(object):defdeleteNode(self,node):IIlIItypenode:ListNodertype:voidDonotreturnanythingmodifynodein-placeinsteadI111fnode=nonepasse⊥se:nextnodenode.nextnodevalnextnodevalnode.nextnextnode,next

Enviro-ThecompletedynamicAAAskyandweathersolution!Veryeasysetup:Thenewmanagercomponentmakesiteasyaspossibletosetupenviroinyourscenes.IncludesEnviroLite!ThisversionincludesEnviroLiteversionaswell.Idealformulti-platformprojects:Useliteversionforlowendplatformlikemobilesandstandardforpcandconsoles.Withoneclickyoucanswitchbetweenenviroversions.ThecentralizedAPIforyourownscriptswillworkforbothversionsofcourse.ProfileSystem:Enviro'snewprofilesystemmakestweakingyourskyaseasyaspossible.Tweaksettingsinruntimeandsavetoprofile.Loadprofilesindesignandruntime.Createdifferentprofilesfordifferentscenesorshareyourconfigurationswithotheruser.Day-Nightcycle:Envirosupportsarealisticday-nightcycle.Withcorrectsunandmoonpositionswithfulllocationsupportwithlatitudeandlongitude.Youhaveoptionstouseyoursystemtimeorletenviroupdatetimebasedonrealtimeminutes.Skybox:Enviroincludesanadvancedfastatmosphericskyboxshadertogetgreatlookingskies!Yougotalotofoptionstotweaktheskyandevencansetupfunkyalienskies!Lighting:Envirowillrealisticlylightyourscenebasedonsunaltitude.Youhavecompletecontroloverlightintensityandcolorbymodifyingcurvesandgradientsrightineditor!Youalsocanchoosebetweendifferentambientlightmodesofcourse.Seasons:Envirowillchangeseasonsandgotacomponentstoswapoutgameobject,materialsandtexturesofunityterrain.Youarenotlimitedtorealisticsettings!Youcansetthestartandenddaysofeachseason.Enviroalsosupportstemperaturesimulation,basedonseason,timeofdayandcurrentweather.Clouds:Environewraymarchingcloudsystemisbasedonlatestcloudrenderingpapers.Thesewillbringyouskytolifeandofferplentyoptionstocustomize.CloudsperformanceisoptimizedbyusingtechsliketemporalreprojectionandLODsystem.Inadditiontherearealsofastflatandparticlecloudsoptionstomixoruseformaximumperformance.Fog:Needstunnishinglookingfog?Enviroincludesanadvancedlightscatteringfogimageeffectwithdistance,heightandskyfogsupport.Needfogonyourtransparentmaterial?Noproblem,withonlyafewlinesofcodeyoucouldmodifyyourowntransparentshaderstobefoggedcorrectly.Andafewparticleandtransparentshadersalreadyincludedtogetyoustarted!Weather:Enviroincludesaverypowerfullweathersystem.Youcancreateyourownweathertypesanddrivelight,sky,fogandclouds.Envirosupportsallkindofunityshurikenparticleeffectstogiveyouthefreedomtocreateanyweathereffectyoucanthinkof.Itincludes11premadeweathertypesincluding:ClearSky,cloudy,raining,stormy,snowyandfoggyweather.Youcanenablelightningstormsandchoosedifferentambientandweathersoundsforeachweatherwithsmoothtransitions.VolumetricLighting:NeedsomevolumelighteffectsyouseeinAAAgames?Noproblem,envirosupportvolumetriclightingfordirectional,pointandspotlightsoutofthebox!SceneViewEffects:Previewenviroeffectslikeclouds,volumelightingandfogdirectlywhileyouworkonyourscenes.Youcanenableordisablesceneviewpreviewforeacheffectindividualofcourse.Networking:EnvirosupportUNet,MirrorandPhotonoutofthebox.Itwillsynchronizetimeandweatherwithallyourplayers.Enviroalsogotanminimalmodeforheadlessserverstoonlycalculatetimeandweatherbutnothingmore.VirtualReality:Envirosupportsmultiandsinglepassstereoscopicrendering!TestedonOculusRift.That'snotall!Enviroincludesalotmoregreatfeatures:*Eventsystemforyougamelogic.*WeatherZones.Createasmanyzoneswiththeirownweatherforyourbiomes.*Orbitingsatellites.*VegetationGrowth.Andyoucanusealotofawesome3rd-partyassetsrightofthebox.Activateandadd3rd-partysupportthroughthenewinterface.Againitseasyandfastaspossible!*GaiaCompatible!*CTSCompatible!*AQUASIntegration!*LUXWaterIntegration!*FogVolume3Integration!*VegetationStudioProIntegration!*PlaymakerActions!*ReliefTerrainShaderIntegration!*UBERShaderIntegration!*MicroSplatIntegration!*MegaSplatShaderIntegration!*LuxShaderIntegration!*PhotonNetworkIntegration!*MirrorNetworkIntegration!*PegasusIntegration!Requirements:*Supportgammaandlinearcolorspace.*Supportforwardanddeferredrendering.*WorkingonDX9,DX11,DX12,OpenGlCore,MetalandVulkan.*VolumetricLightingrequiresatleastshader-model3.5+andwillbedeactivatedonDX9Currentlimitation:*Volumetriccloudsarecurrentlynotsuitableforfly-throughs!Willbeworkedoninlaterupdates!

Inresponsetotheexponentiallyincreasingneedtoanalyzevastamountsofdata,NeuralNetworksforAppliedSciencesandEngineering:FromFundamentalstoComplexPatternRecognitionprovidesscientistswithasimplebutsystematicintroductiontoneuralnetworks.Beginningwithanintroductorydiscussionontheroleofneuralnetworksinscientificdataanalysis,thisbookprovidesasolidfoundationofbasicneuralnetworkconcepts.Itcontainsanoverviewofneuralnetworkarchitecturesforpracticaldataanalysisfollowedbyextensivestep-by-stepcoverageonlinearnetworks,aswellas,multi-layerperceptronfornonlinearpredictionandclassificationexplainingallstagesofprocessingandmodeldevelopmentillustratedthroughpracticalexamplesandcasestudies.LaterchapterspresentanextensivecoverageonSelfOrganizingMapsfornonlineardataclustering,recurrentnetworksforlinearnonlineartimeseriesforecasting,andothernetworktypessuitableforscientificdataanalysis.Withaneasytounderstandformatusingextensivegraphicalillustrationsandmultidisciplinaryscientificcontext,thisbookfillsthegapinthemarketforneuralnetworksformulti-dimensionalscientificdata,andrelatesneuralnetworkstostatistics.FeaturesxExplainsneuralnetworksinamulti-disciplinarycontextxUsesextensivegraphicalillustrationstoexplaincomplexmathematicalconceptsforquickandeasyunderstanding?Examinesin-depthneuralnetworksforlinearandnonlinearprediction,classification,clusteringandforecastingxIllustratesallstagesofmodeldevelopmentandinterpretationofresults,includingdatapreprocessing,datadimensionalityreduction,inputselection,modeldevelopmentandvalidation,modeluncertaintyassessment,sensitivityanalysesoninputs,errorsandmodelparametersSandhyaSamarasingheobtainedherMScinMechanicalEngineeringfromLumumbaUniversityinRussiaandanMSandPhDinEngineeringfromVirginiaTech,USA.

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

详尽的LSTM代码，附带数据。
RNN全称轮回神经收集（RecurrentNeuralNetworks），是用来处置序列数据的。
在传统的神经收集模子中，从输入层到隐含层再到输入层，层与层之间是全毗邻的，每一层之间的节点是无毗邻的。
然则这种普通的神经收集对于许多对于功夫序列的下场却能干有力。

详尽的LSTM代码，附带数据。
RNN全称轮回神经收集（RecurrentNeuralNetworks），是用来处置序列数据的。
在传统的神经收集模子中，从输入层到隐含层再到输入层，层与层之间是全毗邻的，每一层之间的节点是无毗邻的。
然则这种普通的神经收集对于许多对于功夫序列的下场却能干有力。

GRU（GatedRecurrentUnit）神经网络是LSTM的一个变体，GRU在保持了LSTM的效果同时又使结构愈加简单，是一种非常流行RNN神经网络，它只有两个门了，分别为更新门tz和重置门tr。
更新门控制前一时刻的状态信息被带入到当前状态中的程度，值越大前一时刻的状态信息带入越多。
重置门控制忽略前一时刻的状态信息的程度，值越小说明忽略得越多。

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。