ThedatasetiscollectedbyYonseiUniversity.Wedeployedourmobilitymonitoringsystem,namedLifeMap,tocollectmobilitydataovertwomonthsinSeoul,Korea.LifeMapusedlearningschemeproposedinfollowingpaper.Pleasereferthispaperwhenyouuseourdataset.*YohanChon,ElmurodTalipov,HyojeongShin,andHojungCha.2011.Mobilityprediction-basedsmartphoneenergyoptimizationforeverydaylocationmonitoring.InProceedingsofthe9thACMConferenceonEmbeddedNetworkedSensorSystems(SenSys'11).ACM,NewYork,NY,USA,82-95.Visitourhomepageformoreinformation(http://lifemap.yonsei.ac.kr).

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

Byapplyingsupportvectorregression,themodelingdataofriceleavescollectedinourstudyweregroupedintosampletrainingsetandtestset,andthreemachinelearningpredictionmodelsonricegrowingenvironmentagainstleafbladelength,widthandSPADvaluewereconstructed..

ContentsPrefacevTypographicalConventionsxi1Introduction11.1AQuickOverviewofS.......................31.2UsingS...............................51.3AnIntroductorySession......................61.4WhatNext?.............................122DataManipulation132.1Objects...............................132.2Connections.............................202.3DataManipulation.........................272.4TablesandCross-Classification...................373TheSLanguage413.1LanguageLayout..........................413.2MoreonSObjects.........................443.3ArithmeticalExpressions......................473.4CharacterVectorOperations....................513.5FormattingandPrinting.......................543.6CallingConventionsforFunctions.................553.7ModelFormulae...........................563.8ControlStructures..........................583.9ArrayandMatrixOperations....................603.10IntroductiontoClassesandMethods................664Graphics694.1GraphicsDevices..........................714.2BasicPlottingFunctions......................72viiviiiContents4.3EnhancingPlots...........................774.4FineControlofGraphics......................824.5TrellisGraphics...........................895UnivariateStatistics1075.1ProbabilityDistributions......................1075.2GeneratingRandomData......................1105.3DataSummaries...........................1115.4ClassicalUnivariateStatistics....................1155.5RobustSummaries.........................1195.6DensityEstimation.........................1265.7BootstrapandPermutationMethods................1336LinearStatisticalModels1396.1AnAnalysisofCovarianceExample................1396.2ModelFormulaeandModelMatrices...............1446.3RegressionDiagnostics.......................1516.4SafePrediction....................

GoogleDeepMind的DavidSilver的强化学习课程讲义，包括MarkovDecisionProcesses、PlanningbyDynamicProgramming、Model-FreePrediction、Model-FreeControl、FunctionApproximation、PolicyGradientMethods、IntegratingLearningandPlanning、ExplorationandExploitation以及游戏案例分析。
视频：https://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa

Multivariatetimeseriesanalysisconsiderssimultaneouslymultipletimeseries.Itisabranchofmultivariatestatisticalanalysisbutdealsspecificallywithdependentdata.Itis,ingeneral,muchmorecomplicatedthantheunivariatetimeseriesanalysis,especiallywhenthenumberofseriesconsideredislarge.Westudythismorecomplicatedstatisticalanalysisinthisbookbecauseinreallifedecisionsofteninvolvemultipleinter-relatedfactorsorvariables.Understandingtherelationshipsbetweenthosefactorsandprovidingaccuratepredictionsofthosevariablesarevaluableindecisionmaking.Theobjectivesofmultivariatetimeseriesanalysisthusinclude1.Tostudythedynamicrelationshipsbetweenvariables2.Toimprovetheaccuracyofprediction

Kaggle在9月30日开启的一个新的比赛，举办者是巴西最大的汽车与住房保险公司之一：PortoSeguro。
该比赛要求参赛者根据汽车保单持有人的数据建立机器学习模型，分析该持有人是否会在次年提出索赔。
这里的文档是比赛所用到的数据，数据均已经处理

丹麦科技大学教授写的插值工具，Fortran和.exe都有，快捷好用，英文说明如下：programforinterpolatingvaluesfromagridusingbilinearorsplineinterpolation.thegridorthepredictionpointsmaybecineithergeographicalorutmcoordinates.thesplinepredictionisperformedinawindowofsize'nsp'x'nsp'pointsaroundthecwantedpoints,withtypicalvalueofnspbeing8foragoodinterpolation.

著名的Netflix智能推荐百万美金大奖赛使用是数据集.因为竞赛关闭,Netflix官网上已无法下载.Netflixprovidedatrainingdatasetof100,480,507ratingsthat480,189usersgaveto17,770movies.Eachtrainingratingisaquadrupletoftheform.TheuserandmoviefieldsareintegerIDs,whilegradesarefrom1to5(integral)stars.[3]Thequalifyingdatasetcontainsover2,817,131tripletsoftheform,withgradesknownonlytothejury.Aparticipatingteam'salgorithmmustpredictgradesontheentirequalifyingset,buttheyareonlyinformedofthescoreforhalfofthedata,thequizsetof1,408,342ratings.Theotherhalfisthetestsetof1,408,789,andperformanceonthisisusedbythejurytodeterminepotentialprizewinners.Onlythejudgesknowwhichratingsareinthequizset,andwhichareinthetestset—thisarrangementisintendedtomakeitdifficulttohillclimbonthetestset.Submittedpredictionsarescoredagainstthetruegradesintermsofrootmeansquarederror(RMSE),andthegoalistoreducethiserrorasmuchaspossible.Notethatwhiletheactualgradesareintegersintherange1to5,submittedpredictionsneednotbe.Netflixalsoidentifiedaprobesubsetof1,408,395ratingswithinthetrainingdataset.Theprobe,quiz,andtestdatasetswerechosentohavesimilarstatisticalproperties.Insummary,thedatausedintheNetflixPrizelooksasfollows:Trainingset(99,072,112ratingsnotincludingtheprobeset,100,480,507includingtheprobeset)Probeset(1,408,395ratings)Qualifyingset(2,817,131ratings)consistingof:Testset(1,408,789ratings),usedtodeterminewinnersQuizset(1,408,342ratings),usedtocalculateleaderboardscoresForeachmovie,titleandyearofreleaseareprovidedinaseparatedataset.Noinformationatallisprovidedaboutusers.Inordertoprotecttheprivacyofcustomers,"someoftheratingdataforsomecustomersinthetrainingandqualifyin

HandbookofImageQuality,CharacterizationandPrediction.BrianWKeelan.MarcelDekker,2002好不容易找到的

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。