WilliamK.Pratt(Author)ProductDetailsHardcover:720pagesPublisher:Wiley-Interscience;2edition(April1991)Language:EnglishISBN-10:0471857661ISBN-13:978-0471857662ProductDescriptionThisisarevisededitionofatextthatdescribesanimportanttechnologywithscientific,industrial,biomedical,spaceandgovernmentapplications.Muchofthenewmaterialinthisbookdealswithimageanalysis,asubjectinwhichtherehasbeenconsiderableprogressinthelastdecade.

ThisisadraftbyDimitriP.Bertsekas,whofromMIT.Itmaybepublishedin2019byAthenaScientific.ItisagoodresourcetostudyRLandOpt.

Despitetheincreasinguseofcomputers,thebasicneedformathematicaltablescontinues.Tablesserveavitalroleinpreliminarysurveysofproblemsbeforeprogrammingformachineoperation,andtheyareindispensabletothousandsofengineersandscientistswithoutaccesstomachines.Becauseofautomaticcomputers,however,andbecauseofrecentscientificadvances,agreatervarietyoffunctionsandahigheraccuracyoftabulationthanhavebeenavailableuntilnowarerequired.In1954,aconferenceonmathematicaltables,sponsoredbyM.I.T.andtheNationalScienceFoundation,mettodiscussamodernizationandextensionofJahnkeandEmde'sclassicaltablesoffunctions.Thisvolume,published10yearslaterbytheU.S.DepartmentofCommerce,istheresult.Designedtoincludeamaximumofinformationandtomeettheneedsofscientistsinallfields,itisamonumentalpieceofwork,acomprehensiveandself-containedsummaryofthemathematicalfunctionsthatariseinphysicalandengineeringproblems.Thebookcontains29setsoftables,sometoashighas20places:mathematicalconstants;physicalconstantsandconversionfactors(6tables);exponentialintegralandrelatedfunctions(7);errorfunctionandFresnelintegrals(12);Besselfunctionsofinteger(12)andfractional(13)order;integralsofBesselfunctions(2);Struveandrelatedfunctions(2);confluenthypergeometricfunctions(2);Coulombwavefunctions(2);hypergeometricfunctions;Jacobianellipticandthetafunctions(2);ellipticintegrals{9);Weierstrassellipticandrelatedfunctions;paraboliccylinderfunctions{3);Mathieufunctions(2);spheroidalwavefunctions(5);orthogonalpolynomials(13);combinatorialanalysis(9);numericalinterpolation,differentiationandintegration(11);probabilityfunctions(ll);scalesofnotation(6);miscellaneousfunctions(9);Laplacetransforms(2);andothers.Eachofthesesectionsisprefacedbyalistofrelatedformulasandgraph

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PracticalGraphAnalyticswithApacheGiraphhelpsyoubuilddataminingandmachinelearningapplicationsusingtheApacheFoundation'sGiraphframeworkforgraphprocessing.ThisisthesameframeworkasusedbyFacebook,Google,andothersocialmediaanalyticsoperationstoderivebusinessvaluefromvastamountsofinterconnecteddatapoints.Graphsariseinawealthofdatascenariosanddescribetheconnectionsthatarenaturallyformedinbothdigitalandrealworlds.ExamplesofsuchconnectionsaboundinonlinesocialnetworkssuchasFacebookandTwitter,amonguserswhoratemoviesfromserviceslikeNetflixandAmazonPrime,andareusefuleveninthecontextofbiologicalnetworksforscientificresearch.Whetherinthecontextofbusinessorscience,viewingdataasconnectedaddsvaluebyincreasingtheamountofinformationavailabletobedrawnfromthatdataandputtouseingeneratingnewrevenueorscientificopportunities.ApacheGiraphoffersasimpleyetflexibleprogrammingmodeltargetedtographalgorithmsanddesignedtoscaleeasilytoaccommodatemassiveamountsofdata.OriginallydevelopedatYahoo!,Giraphisnowatoptop-levelprojectattheApacheFoundation,anditenlistscontributorsfromcompaniessuchasFacebook,LinkedIn,andTwitter.

FEniCSisapopularopen-source(LGPLv3)computingplatformforsolvingpartialdifferentialequations(PDEs).FEniCSenablesuserstoquicklytranslatescientificmodelsintoefficientfiniteelementcode.Withthehigh-levelPythonandC++interfacestoFEniCS,itiseasytogetstarted,butFEniCSoffersalsopowerfulcapabilitiesformoreexperiencedprogrammers.FEniCSrunsonamultitudeofplatformsrangingfromlaptopstohigh-performanceclusters.

WeinbergdistillstheessencefromvonBertalanffy'sclassicandmanagestopresentitinaveryaccessiblefashion.Thebookhasbeenoutofprintforquiteawhilesoitisgreattoseeanewedition.Themessageandinformationcontainedinhere,althoughoriginallypublishedin1975,isnowmorerelevantthanever.Weinbergcoversmanyaspectsofsystemstheorybeginningwiththemainstumblingblockwiththepresentscientificparadigm:theideathattheuniverseismechanistic.HistreatmentismuchmoregeneralthanRobertRosen'sin"LifeItself"butstillconveyswhythemechanisticnotionisflawed.Hethenoutlinesthegeneralsystemstheoryapproachbeforeleadingintotheideathatasystemissimplyawayoflookingattheworld.Hethenoutlinestheprincipleofindifference.Thisleadsstraightintotwosectionsoutliningvariousaspectsofmakingobservations.Finallyhediscussesbehaviourandthensomegeneralsystemsquestions.Throughoutthebookheusesmanyexamplesfromdisparatefieldsinconjunctionwithquestionsforfurtherresearch.Itisgreattoseesomeonewhodoesn'tpreachsystemsbutactuallyusestheideas.Definitelyamust-readaswedecidedhowtosolvethemyriadofissuesbeforeus.

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.

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