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

国外经典随机过程教材Probability_Random_Variables_and_Stochastic_Process_4th，概率随机变量随机过程（第四版）习题解答，对深入学习随机过程，特别是通信专业很有帮助。

Probability-dependentH"~synchronizationcontrolfordynamicalnetworkswithrandomlyvaryingnonlinearities

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

Probability-DependentGain-ScheduledFilteringforStochasticSystemsWithMissingMeasurements

Probability,Statistics,andRandomProcessesforEngineers(4thEdition).pdf

[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

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

Advanced+Probability+Theory(荆炳义+高等概率论)

统计学概论8版的solutionProbability&Statisticsforengineersandscientists8thsolution

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。