模型预测、自动控制领域大牛AlbertoBemporad的博士课程讲义，内容为MPC模型预测控制，讲解了MPC的基本概念，线性系统的MPC理论

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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....................

MasteringPredictiveAnalyticswithR(2nd)英文无水印pdf第2版pdf转化版，非原版pdfpdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开本资源转载自网络，如有侵权，请联系上传者或csdn删除本资源转载自网络，如有侵权，请联系上传者或csdn删除

Thetime-temperaturesuperpositionprinciplehasbeenappliedtopredictaccuratelythelong-termviscoelasticbehaviorofamorphousresinatatemperaturebelowtheglasstransitiontemperaturefrommeasuringtheshort-termviscoelasticbehavioratelevatedtemperatures.Asimplifiedmetho

ExploratoryDataAnalysisUsingRprovidesaclassroom-testedintroductiontoexploratorydataanalysis(EDA)andintroducestherangeof"interesting"–good,bad,andugly–featuresthatcanbefoundindata,andwhyitisimportanttofindthem.ItalsointroducesthemechanicsofusingRtoexploreandexplaindata.Thebookbeginswithadetailedoverviewofdata,exploratoryanalysis,andR,aswellasgraphicsinR.Itthenexploresworkingwithexternaldata,linearregressionmodels,andcraftingdatastories.ThesecondpartofthebookfocusesondevelopingRprograms,includinggoodprogrammingpracticesandexamples,workingwithtextdata,andgeneralpredictivemodels.Thebookendswithachapteron"keepingitalltogether"thatincludesmanagingtheRinstallation,managingfiles,documenting,andanintroductiontoreproduciblecomputing.Thebookisdesignedforbothadvancedundergraduate,entry-levelgraduatestudents,andworkingprofessionalswithlittletonopriorexposuretodataanalysis,modeling,statistics,orprogramming.itkeepsthetreatmentrelativelynon-mathematical,eventhoughdataanalysisisaninherentlymathematicalsubject.Exercisesareincludedattheendofmostchapters,andaninstructor'ssolutionmanualisavailable.AbouttheAuthor:RonaldK.PearsonholdsthepositionofSeniorDataScientistwithGeoVera,apropertyinsurancecompanyinFairfield,California,andhehaspreviouslyheldsimilarpositionsinavarietyofapplicationareas,includingsoftwaredevelopment,drugsafetydataanalysis,andtheanalysisofindustrialprocessdata.HeholdsaPhDinElectricalEngineeringandComputerSciencefromtheMassachusettsInstituteofTechnologyandhaspublishedconferenceandjournalpapersontopicsrangingfromnonlineardynamicmodelstructureselectiontotheproblemsofdisguisedmissingdatainpredictivemodeling.Dr.Pearsonhasauthoredorco-authoredbooksincludingExploringDatainEngineeri

讲述alphazero的原文，发表在nature。
Along-standinggoalofartificialintelligenceisanalgorithmthatlearns,tabularasa,superhumanproficiencyinchallengingdomains.Recently,AlphaGobecamethefirstprogramtodefeataworldchampioninthegameofGo.ThetreesearchinAlphaGoevaluatedpositionsandselectedmovesusingdeepneuralnetworks.Theseneuralnetworksweretrainedbysupervisedlearningfromhumanexpertmoves,andbyreinforcementlearningfromself-play.Hereweintroduceanalgorithmbasedsolelyonreinforcementlearning,withouthumandata,guidanceordomainknowledgebeyondgamerules.AlphaGobecomesitsownteacher:aneuralnetworkistrainedtopredictAlphaGo’sownmoveselectionsandalsothewinnerofAlphaGo’sgames.Thisneuralnetworkimprovesthestrengthofthetreesearch,resultinginhigherqualitymoveselectionandstrongerself-playinthenextiteration.Startingtabularasa,ournewprogramAlphaGoZeroachievedsuperhumanperformance,winning100–0againstthepreviouslypublished,champion-defeatingAlphaGo.

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

常用字典的目录2019/02/2610:28Blasting_dictionary2019/03/2915:58RW_Password-master2019/03/2209:25SSH爆破2019/04/1811:08211,025大哥字典.txt2019/02/2610:27常用用户名及密码2019/03/2209:161,047,492弱口令字典.txt2020/03/1614:06用户名密码字典3个文件1,258,517字节常用字典\Blasting_dictionary的目录2017/11/1515:302,3443389爆破字典.txt2019/02/1809:04a5+pudn源代码目录103W+2017/11/1515:303,637baopo.py2017/11/1515:3054big_dictionary.txt2017/11/1515:30500jiahouzhui.py2017/11/1515:3018,284liunx_users_dictionaries.txt2017/11/1515:305,176NT密码.txt2017/11/1515:308,846renkoutop.txt2017/11/1515:301,015top100password.txt2017/11/1515:309,273,839top10W.txt2017/11/1515:3022,534top500姓名组合.txt2017/11/1515:30134weblogic默认密码列表.txt2017/11/1515:30144,194webshellPassword.txt2019/02/1809:04各类黑客大牛后门密码2017/11/1515:3040,710后台路径.txt2017/11/1515:302,107字典.txt2017/11/1515:3062,027常用密码.txt2017/11/1515:3071,357常用用户名.txt2019/02/1809:04撞库邮箱和密码2017/11/1515:3010,197数据库地址.txt2017/11/1515:30174,888渗透字典.txt2019/02/1809:04目录2017/11/1515:3017,830突破密码.txt2019/02/1809:04美国人字典2017/11/1515:301,616自己收集的密码.txt2017/11/1515:301,101装机密码.txt21个文件9,862,390字节常用字典\Blasting_dictionary\a5+pudn源代码目录103W+的目录2017/11/1515:3033,719,767全部.txt2019/02/1809:04分类1个文件33,719,767字节常用字典\Blasting_dictionary\a5+pudn源代码目录103W+\分类的目录2017/11/1515:3081,3761.pack.txt2017/11/1515:30

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。