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

Thisbookcoversalgorithmicandhardwareimplementationtechniquestoenableembeddeddeeplearning.Theauthorsdescribesynergeticdesignapproachesontheapplication-,algorithmic-,computerarchitecture-,andcircuit-levelthatwillhelpinachievingthegoalofreducingthecomputationalcostofdeeplearningalgorithms.Theimpactofthesetechniquesisdisplayedinfoursiliconprototypesforembeddeddeeplearning.Givesawideoverviewofaseriesofeffectivesolutionsforenergy-efficientneuralnetworksonbatteryconstrainedwearabledevices;Discussestheoptimizationofneuralnetworksforembeddeddeploymentonalllevelsofthedesignhierarchy–applications,algorithms,hardwarearchitectures,andcircuits–supportedbyrealsiliconprototypes;ElaboratesonhowtodesignefficientConvolutionalNeuralNetworkprocessors,exploitingparallelismanddata-reuse,sparseoperations,andlow-precisioncomputations;Supportstheintroducedtheoryanddesignconceptsbyfourrealsiliconprototypes.Thephysicalrealization’simplementationandachievedperformancesarediscussedelaboratelytoillustratedandhighlighttheintroducedcross-layerdesignconcepts.

successful-algorithmic-trading中文与英文版(含代码）

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