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Datavisualizationisoneofthemostimportantpartofdatascience.Manybooksandcoursespresentacatalogueofgraphicsbuttheydon'tteachyouwhichchartstouseaccordingtothetypeofthedata.Inthisbook,westartbypresentingthekeygraphicsystemsandpackagesavailableinR,includingRbasegraphs,latticeandggplot2plottingsystems.Next,weprovidemorethan200practicalexamplestocreategreatgraphicsfortherightdatausingeithertheggplot2packageandextensionsorthetraditionalRgraphics.Withthisbook,you'lllearn:-Howtoquicklycreatebeautifulgraphicsusingggplot2packages-Howtoproperlycustomizeandannotatetheplots-Typeofgraphicsforvisualizingcategoricalandcontinuousvariables-Howtoaddautomaticallyp-valuestoboxplots,barplotsandalternatives-Howtoaddmarginaldensityplotsandcorrelationcoefficientstoscatterplots-Keymethodsforanalyzingandvisualizingmultivariatedata-Rfunctionsandpackagesforplottingtimeseriesdata-Howtocombinemultipleplotsononepagetocreateproduction-qualityfigures.

Inresponsetotheexponentiallyincreasingneedtoanalyzevastamountsofdata,NeuralNetworksforAppliedSciencesandEngineering:FromFundamentalstoComplexPatternRecognitionprovidesscientistswithasimplebutsystematicintroductiontoneuralnetworks.Beginningwithanintroductorydiscussionontheroleofneuralnetworksinscientificdataanalysis,thisbookprovidesasolidfoundationofbasicneuralnetworkconcepts.Itcontainsanoverviewofneuralnetworkarchitecturesforpracticaldataanalysisfollowedbyextensivestep-by-stepcoverageonlinearnetworks,aswellas,multi-layerperceptronfornonlinearpredictionandclassificationexplainingallstagesofprocessingandmodeldevelopmentillustratedthroughpracticalexamplesandcasestudies.LaterchapterspresentanextensivecoverageonSelfOrganizingMapsfornonlineardataclustering,recurrentnetworksforlinearnonlineartimeseriesforecasting,andothernetworktypessuitableforscientificdataanalysis.Withaneasytounderstandformatusingextensivegraphicalillustrationsandmultidisciplinaryscientificcontext,thisbookfillsthegapinthemarketforneuralnetworksformulti-dimensionalscientificdata,andrelatesneuralnetworkstostatistics.FeaturesxExplainsneuralnetworksinamulti-disciplinarycontextxUsesextensivegraphicalillustrationstoexplaincomplexmathematicalconceptsforquickandeasyunderstanding?Examinesin-depthneuralnetworksforlinearandnonlinearprediction,classification,clusteringandforecastingxIllustratesallstagesofmodeldevelopmentandinterpretationofresults,includingdatapreprocessing,datadimensionalityreduction,inputselection,modeldevelopmentandvalidation,modeluncertaintyassessment,sensitivityanalysesoninputs,errorsandmodelparametersSandhyaSamarasingheobtainedherMScinMechanicalEngineeringfromLumumbaUniversityinRussiaandanMSandPhDinEngineeringfromVirginiaTech,USA.

Author:WesMcKinneyISBN-10:1491957662Year:2017Pages:544Language:EnglishFileformat:PDFGetcompleteinstructionsformanipulating,processing,cleaning,andcrunchingdatasetsinPython.UpdatedforPython3.6,thesecondeditionofthishands-onguideispackedwithpracticalcasestudiesthatshowyouhowtosolveabroadsetofdataanalysisproblemseffectively.You’lllearnthelatestversionsofpandas,NumPy,IPython,andJupyterintheprocess.WrittenbyWesMcKinney,thecreatorofthePythonpandasproject,thisbookisapractical,modernintroductiontodatasciencetoolsinPython.It’sidealforanalystsnewtoPythonandforPythonprogrammersnewtodatascienceandscientificcomputing.DatafilesandrelatedmaterialareavailableonGitHub.UsetheIPythonshellandJupyternotebookforexploratorycomputingLearnbasicandadvancedfeaturesinNumPy(NumericalPython)GetstartedwithdataanalysistoolsinthepandaslibraryUseflexibletoolstoload,clean,transform,merge,andreshapedataCreateinformativevisualizationswithmatplotlibApplythepandasgroupbyfacilitytoslice,dice,andsummarizedatasetsAnalyzeandmanipulateregularandirregulartimeseriesdataLearnhowtosolvereal-worlddataanalysisproblemswiththorough,detailedexamples

brockwell功夫序列实际与方式第一版+第二版（中文版）

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2014KDD论文“CorrelatingEventswithTimeSeriesforIncidentDiagnosis”的学习与讲授。

function[E,V,A,C]=eeof(X,M,convert)%Syntax:[E,V,A,C]=eeof(X,M);[E,V,A,C]=eeof(X,M,1);%Thisfunctionperformsanextendedempiricalorthogonal%function(EEOF)analysisofmatrix'X',forembeddingdimension'M'.%EachoftheLcolumnsofXisatimeseriesoflengthN.%%Returns:E-eigenfunctionmatrix.(LMbyLM)%V-vectorcontainingvariances(unnormalizedeigenvalues).%A-matrixofprincipalcomponents.%C-lag-covariancematrix.

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