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

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Title:MachineLearning:AnAlgorithmicPerspective,2ndEditionAuthor:StephenMarslandLength:457pagesEdition:2Language:EnglishPublisher:ChapmanandHall/CRCPublicationDate:2014-10-08ISBN-10:1466583282ISBN-13:9781466583283AProven,Hands-OnApproachforStudentswithoutaStrongStatisticalFoundationSincethebest-sellingfirsteditionwaspublished,therehavebeenseveralprominentdevelopmentsinthefieldofmachinelearning,includingtheincreasingworkonthestatisticalinterpretationsofmachinelearningalgorithms.Unfortunately,computersciencestudentswithoutastrongstatisticalbackgroundoftenfindithardtogetstartedinthisarea.Remedyingthisdeficiency,MachineLearning:AnAlgorithmicPerspective,SecondEditionhelpsstudentsunderstandthealgorithmsofmachinelearning.Itputsthemonapathtowardmasteringtherelevantmathematicsandstatisticsaswellasthenecessaryprogrammingandexperimentation.NewtotheSecondEditionTwonewchaptersondeepbeliefnetworksandGaussianprocessesReorganizationofthechapterstomakeamorenaturalflowofcontentRevisionofthesupportvectormachinematerial,includingasimpleimplementationforexperimentsNewmaterialonrandomforests,theperceptronconvergencetheorem,accuracymethods,andconjugategradientoptimizationforthemulti-layerperceptronAdditionaldiscussionsoftheKalmanandparticlefiltersImprovedcode,includingbetteruseofnamingconventionsinPythonSuitableforbothanintroductoryone-semestercourseandmoreadvancedcourses,thetextstronglyencouragesstudentstopracticewiththecode.Eachchapterincludesdetailedexamplesalongwithfurtherreadingandproblems.Allofthecodeusedtocreatetheexamplesisavailableontheauthor’swebsite.TableofContentsChapter1:IntroductionChapter2:PreliminariesChapter3:Neurons,NeuralNetworks,andLinearDiscriminantsChapter4:TheMulti-layerPerceptronChapter5:R

PublicationDate:November17,2010|ISBN-10:1441977864|ISBN-13:978-1441977861|Edition:2011Financialengineershaveaccesstoenormousquantitiesofdatabutneedpowerfulmethodsforextractingquantitativeinformation,particularlyaboutvolatilityandrisks.Keyfeaturesofthistextbookare:illustrationofconceptswithfinancialmarketsandeconomicdata,RLabswithreal-dataexercises,andintegrationofgraphicalandanalyticmethodsformodelinganddiagnosingmodelingerrors.Despitesomeoverlapwiththeauthor'sundergraduatetextbookStatisticsandFinance:AnIntroduction,thisbookdiffersfromthatearliervolumeinseveralimportantaspects:itisgraduate-level;computationsandgraphicsaredoneinR;andmanyadvancedtopicsarecovered,forexample,multivariatedistributions,copulas,Bayesiancomputations,VaRandexpectedshortfall,andcointegration.Theprerequisitesarebasicstatisticsandprobability,matricesandlinearalgebra,andcalculus.Someexposuretofinanceishelpful.

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