DetailsTitle:DigitalImageProcessing,4thEditionAuthor:RafaelC.Gonzalez,RichardE.WoodsLength:1192pagesEdition:4Language:EnglishPublisher:PearsonPublicationDate:2017-03-30ISBN-10:0133356728ISBN-13:9780133356724SalesRank:#680902(SeeTop100Books)CategoriesComputers&TechnologyEngineering&TransportationEngineeringTextbooksDescriptionIntroduceyourstudentstoimageprocessingwiththeindustry’smostprizedtextFor40years,ImageProcessinghasbeenthefoundationaltextforthestudyofdigitalimageprocessing.Thebookissuitedforstudentsatthecollegeseniorandfirst-yeargraduatelevelwithpriorbackgroundinmathematicalanalysis,vectors,matrices,probability,statistics,linearsystems,andcomputerprogramming.Asinallearliereditions,thefocusofthiseditionofthebookisonfundamentals.The4thEdition,whichcelebratesthebook’s40thanniversary,isbasedonanextensivesurveyoffaculty,students,andindependentreadersin150institutionsfrom30countries.Theirfeedbackledtoexpandedornewcoverageoftopicssuchasdeeplearninganddeepneuralnetworks,includingconvolutionalneuralnets,thescale-invariantfeaturetransform(SIFT),maximally-stableextremalregions(MSERs),graphcuts,k-meansclusteringandsuperpixels,activecontours(snakesandlevelsets),andexacthistogrammatching.?Majorimprovementsweremadeinreorganizingthematerialonimagetransformsintoamorecohesivepresentation,andinthediscussionofspatialkernelsandspatialfiltering.?Majorrevisionsandadditionsweremadetoexamplesandhomeworkexercisesthroughoutthebook.Forthefirsttime,weaddedMATLABprojectsattheendofeverychapter,andcompiledsupportpackagesforyouandyourteachercontaining,solutions,imagedatabases,andsamplecode.Thesupportmaterialsforthistitlecanbefoundatwww.ImageProcessingPlace.com

PrefaceIwrotethisbooktohelpmachinelearningpractitioners,likeyou,getontopoflinearalgebra,fast.LinearAlgebraIsImportantinMachineLearningThereisnodoubtthatlinearalgebraisimportantinmachinelearning.Linearalgebraisthemathematicsofdata.It’sallvectorsandmatricesofnumbers.Modernstatisticsisdescribedusingthenotationoflinearalgebraandmodernstatisticalmethodsharnessthetoolsoflinearalgebra.Modernmachinelearningmethodsaredescribedthesameway,usingthenotationsandtoolsdrawndirectlyfromlinearalgebra.Evensomeclassicalmethodsusedinthefield,suchaslinearregressionvialinearleastsquaresandsingular-valuedecomposition,arelinearalgebramethods,andothermethods,suchasprincipalcomponentanalysis,werebornfromthemarriageoflinearalgebraandstatistics.Toreadandunderstandmachinelearning,youmustbeabletoreadandunderstandlinearalgebra.PractitionersStudyLinearAlgebraTooEarlyIfyouaskhowtogetstartedinmachinelearning,youwillverylikelybetoldtostartwithlinearalgebra.Weknowthatknowledgeoflinearalgebraiscriticallyimportant,butitdoesnothavetobetheplacetostart.Learninglinearalgebrafirst,thencalculus,probability,statistics,andeventuallymachinelearningtheoryisalongandslowbottom-uppath.Abetterfitfordevelopersistostartwithsystematicproceduresthatgetresults,andworkbacktothedeeperunderstandingoftheory,usingworkingresultsasacontext.Icallthisthetop-downorresults-firstapproachtomachinelearning,andlinearalgebraisnotthefirststep,butperhapsthesecondorthird.PractitionersStudyTooMuchLinearAlgebraWhenpractitionersdocirclebacktostudylinearalgebra,theylearnfarmoreofthefieldthanisrequiredfororrelevanttomachinelearning.Linearalgebraisalargefieldofstudythathastendrilsintoengineering,physicsandquantumphysics.Therearealso

经典教材的详细答案，让你学习不在苦恼，不在彷徨，让你赢在起跑线上。

Probability,Statistics,andRandomProcessesforEngineers(4thEdition).pdf

IntroductiontoMathematicalStatistics(7thEdition)byRobertV.Hogg,JoesephMcKean,AllenTCraighttp://www.amazon.com/Introduction-Mathematical-Statistics-7th-Edition/dp/0321795431

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

第七版Indroduction_to_Mathematica_Statistics.Hogg,McKean,Craigseventhedition.pdfUIUC的STAT401统计课程教材ContentsPrefaceix1ProbabilityandDistributions11.1Introduction................................11.2SetTheory................................31.3TheProbabilitySetFunction......................101.4ConditionalProbabilityandIndependence...............211.5RandomVariables............................321.6DiscreteRandomVariables.......................401.6.1Transformations.........................421.7ContinuousRandomVariables......................441.7.1Transformations.........................461.8ExpectationofaRandomVariable...................521.9SomeSpecialExpectations.......................571.10ImportantInequalities..........................682MultivariateDistributions732.1DistributionsofTwoRandomVariables................732.1.1Expectation............................792.2Transformations:BivariateRandomVariables.............842.3ConditionalDistributionsandExpectations..............942.4TheCorrelationCoefficient.......................1022.5IndependentRandomVariables.....................1102.6ExtensiontoSeveralRandomVariables................1172.6.1∗MultivariateVariance-CovarianceMatrix...........1232.7TransformationsforSeveralRandomVariables............1262.8LinearCombinationsofRandomVariables...............1343SomeSpecialDistributions1393.1TheBinomialandRelatedDistributions................1393.2ThePoissonDistribution........................1503.3TheΓ,χ2,andβDistributions.....................1563.4TheNormalDistribution.........................1683.4.1ContaminatedNormals.....................174vviContents3.5TheMultivariateNormalDistribution.................1783.5.1∗Applications...........................1853.6t-andF-Distributions

Masterthefundamentalsofdiscretemathematicsandproof-writingwithMATHEMATICS:ADISCRETEINTRODUCTION!Withaclearpresentation,themathematicstextteachesyounotonlyhowtowriteproofs,buthowtothinkclearlyandpresentcaseslogicallybeyondthiscourse.Thoughitispresentedfromamathematician'sperspective,youwilllearntheimportanceofdiscretemathematicsinthefieldsofcomputerscience,engineering,probability,statistics,operationsresearch,andotherareasofappliedmathematics.Toolssuchhintsandprooftemplatesprepareyoutosucceedinthiscourse.

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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。