[奥莱理]DoingDataScience(英文版)[奥莱理]DoingDataScienceStraightTalkfromtheFrontline(E-Book)☆图书概要：☆Nowthatpeopleareawarethatdatacanmakethedifferenceinanelectionorabusinessmodel,datascienceasanoccupationisgainingground.Buthowcanyougetstartedworkinginawide-ranging,interdisciplinaryfieldthat’ssocloudedinhype?Thisinsightfulbook,basedonColumbiaUniversity’sIntroductiontoDataScienceclass,tellsyouwhatyouneedtoknow.Inmanyofthesechapter-longlectures,datascientistsfromcompaniessuchasGoogle,Microsoft,andeBaysharenewalgorithms,methods,andmodelsbypresentingcasestudiesandthecodetheyuse.Ifyou’refamiliarwithlinearalgebra,probability,andstatistics,andhaveprogrammingexperience,thisbookisanidealintroductiontodatascience.☆出版信息：☆[作者信息]RachelSchutt,CathyO'Neil[出版机构]奥莱理[出版日期]2013年10月31日[图书页数]406页[图书语言]英语[图书格式]PDF格式

SolutionsManualforstatisticalandadaptivesignalprocessing[美]DimitrisG.Manolakis\VinayK.Ingle\StephenM.Kogon统计与自适应信号处理【美】DimitrisG.Manolakis等著全书课后习题详细解答，共467页。

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

☆资源说明：☆[PacktPublishing]RStudio入门教程(R语言统计分析计算)(英文版)[PacktPublishing]LearningRStudioforRStatisticalComputing(E-Book)☆出版信息：☆[作者信息]MarkP.J.vanderLoo,EdwindeJonge[出版机构]PacktPublishing[出版日期]2012年12月24日[图书页数]126页[图书语言]英语[图书格式]PDF格式

CategoricalDataAnalysis,ThirdEditionsummarizesthelatestmethodsforunivariateandcorrelatedmultivariatecategoricalresponses.ReaderswillfindaunifiedgeneralizedlinearmodelsapproachthatconnectslogisticregressionandPoissonandnegativebinomialloglinearmodelsfordiscretedatawithnormalregressionforcontinuousdata.ALANAGRESTIisDistinguishedProfessorEmeritusintheDepartmentofStatisticsattheUniversityofFlorida.Hehaspresentedshortcoursesoncategoricaldatamethodsinthirtycountries.

Writtenbypioneersoftheconcept,thisisthefirstcompleteguidetothephysicalandengineeringprinciplesofMassiveMIMO.Assumingonlyabasicbackgroundincommunicationsandstatisticalsignalprocessing,itwillguidereadersthroughkeytopicssuchaspropagationmodels,channelmodeling,andmulti-cellperformanceanalyses.Theauthors’uniquecapacity-boundapproachwillenablereaderstocarryoutmoreeffectivesystemperformanceanalysisanddevelopadvancedMassiveMIMOtechniquesandalgorithms.Numerouscasestudies,aswellasproblemsetsandsolutionsaccompanyingthebookonline,willhelpreadersputknowledgeintopracticeandacquiretheskillsetneededtodesignandanalyzecomplexwirelesscommunicationsystems.Whetheryouareagraduatestudent,researcher,orindustryprofessionalworkinginthefieldofwirelesscommunications,thiswillbeanindispensableguideforyearstocome.

TheJupyterNotebookallowsyoutocreateandsharedocumentsthatcontainlivecode,equations,visualizations,andexplanatorytext.TheJupyterNotebooksystemisextensivelyusedindomainssuchasdatacleaningandtransformation,numericalsimulation,statisticalmodeling,andmachinelearning.LearningJupyter5willhelpyougettogripswithinteractivecomputingusingreal-worldexamples.ThebookstartswithadetailedoverviewoftheJupyterNotebooksystemanditsinstallationindifferentenvironments.Next,youwilllearntointegratetheJupytersystemwithdifferentprogramminglanguagessuchasR,Python,Java,JavaScript,andJulia,andexplorevariousversionsandpackagesthatarecompatiblewiththeNotebooksystem.Movingahead,youwillmasterinteractivewidgetsandnamespacesandworkwithJupyterinamulti-usermode.Bytheendofthisbook,youwillhaveusedJupyterwithabigdatasetandbeabletoapplyallthefunctionalitiesyou’veexploredthroughoutthebook.YouwillalsohavelearnedallabouttheJupyterNotebookandbeabletostartperformingdatatransformation,numericalsimulation,anddatavisualization.

TheElementsofStatisticalLearningTheElementsofStatisticalLearningTheElementsofStatisticalLearningTheElementsofStatisticalLearning

信号检测与估计的参考书，英文原版Fundamentals-Of-Statistical-Signal-Processing-Estimation-Theory-KayStevenM.Kay

概率论与数理统计经典教材ProbabilityAndStatisticsForEngineeringAndTheSciences(JayL.Devore)5thEd.-SolutionManual答案

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。