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Keras 2.x Projects
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Keras 2.x Projects

Keras2.xProjectsexplainshowtoleveragethepowerofKerastobuildandtrainstate-of-the-artdeeplearningmodelsthroughaseriesofpracticalprojectsthatlookatarangeofreal-worldapplicationareas.Tobeginwith,youwillquicklysetupadeeplearningenvironmentbyinstallingtheKeraslibrary.Througheachoftheprojects,youwillexploreandlearntheadvancedconceptsofdeeplearningandwilllearnhowtocomputeandrunyourdeeplearningmodelsusingtheadvancedofferingsofKeras.Youwilltrainfully-connectedmultilayernetworks,convolutionalneuralnetworks,recurrentneuralnetworks,autoencodersandgenerativeadversarialnetworksusingreal-worldtrainingdatasets.Theprojectsyouwillundertakeareallbasedonreal-worldscenariosofallcomplexitylevels,coveringtopicssuchaslanguagerecognition,stockvolatility,energyconsumptionprediction,fasterobjectclassificationforself-drivingvehicles,andmore.Bytheendofthisbook,youwillbewellversedwithdeeplearninganditsimplementationwithKeras.Youwillhavealltheknowledgeyouneedtotrainyourowndeeplearningmodelstosolvedifferentkindsofproblems.

Giuseppe Ciaburro ·自動化 ·9.6萬字

R Machine Learning Projects
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R Machine Learning Projects

Risoneofthemostpopularlanguageswhenitcomestoperformingcomputationalstatistics(statisticalcomputing)easilyandexploringthemathematicalsideofmachinelearning.Withthisbook,youwillleveragetheRecosystemtobuildefficientmachinelearningapplicationsthatcarryoutintelligenttaskswithinyourorganization.Thisbookwillhelpyoutestyourknowledgeandskills,guidingyouonhowtobuildeasilythroughtocomplexmachinelearningprojects.Youwillfirstlearnhowtobuildpowerfulmachinelearningmodelswithensemblestopredictemployeeattrition.Next,you’llimplementajokerecommendationengineandlearnhowtoperformsentimentanalysisonAmazonreviews.You’llalsoexploredifferentclusteringtechniquestosegmentcustomersusingwholesaledata.Inadditiontothis,thebookwillgetyouacquaintedwithcreditcardfrauddetectionusingautoencoders,andreinforcementlearningtomakepredictionsandwinonacasinoslotmachine.Bytheendofthebook,youwillbeequippedtoconfidentlyperformcomplextaskstobuildresearchandcommercialprojectsforautomatedoperations.

Dr. Sunil Kumar Chinnamgari ·自動化 ·7.1萬字

Hands-On Artificial Intelligence for IoT
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Hands-On Artificial Intelligence for IoT

Therearemanyapplicationsthatusedatascienceandanalyticstogaininsightsfromterabytesofdata.Theseapps,however,donotaddressthechallengeofcontinuallydiscoveringpatternsforIoTdata.InHands-OnArtificialIntelligenceforIoT,wecovervariousaspectsofartificialintelligence(AI)anditsimplementationtomakeyourIoTsolutionssmarter.ThisbookstartsbycoveringtheprocessofgatheringandpreprocessingIoTdatagatheredfromdistributedsources.YouwilllearndifferentAItechniquessuchasmachinelearning,deeplearning,reinforcementlearning,andnaturallanguageprocessingtobuildsmartIoTsystems.YouwillalsoleveragethepowerofAItohandlereal-timedatacomingfromwearabledevices.Asyouprogressthroughthebook,techniquesforbuildingmodelsthatworkwithdifferentkindsofdatageneratedandconsumedbyIoTdevicessuchastimeseries,images,andaudiowillbecovered.UsefulcasestudiesonfourmajorapplicationareasofIoTsolutionsareakeyfocalpointofthisbook.Intheconcludingchapters,youwillleveragethepowerofwidelyusedPythonlibraries,TensorFlowandKeras,tobuilddifferentkindsofsmartAImodels.Bytheendofthisbook,youwillbeabletobuildsmartAI-poweredIoTappswithconfidence.

Amita Kapoor ·自動化 ·7.5萬字

Machine Learning Quick Reference
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Machine Learning Quick Reference

Machinelearningmakesitpossibletolearnabouttheunknownsandgainhiddeninsightsintoyourdatasetsbymasteringmanytoolsandtechniques.Thisbookguidesyoutodojustthatinaverycompactmanner.Aftergivingaquickoverviewofwhatmachinelearningisallabout,MachineLearningQuickReferencejumpsrightintoitscorealgorithmsanddemonstrateshowtheycanbeappliedtoreal-worldscenarios.Frommodelevaluationtooptimizingtheirperformance,thisbookwillintroduceyoutothebestpracticesinmachinelearning.Furthermore,youwillalsolookatthemoreadvancedaspectssuchastrainingneuralnetworksandworkwithdifferentkindsofdata,suchastext,time-series,andsequentialdata.Advancedmethodsandtechniquessuchascausalinference,deepGaussianprocesses,andmorearealsocovered.Bytheendofthisbook,youwillbeabletotrainfast,accuratemachinelearningmodelsatyourfingertips,whichyoucaneasilyuseasapointofreference.

Rahul Kumar ·自動化 ·4.7萬字

Implementing AWS：Design，Build，and Manage your Infrastructure
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Implementing AWS：Design，Build，and Manage your Infrastructure

WiththisLearningPath,you’llexploretechniquestoeasilymanageapplicationsontheAWScloud.You’llbeginwithanintroductiontoserverlesscomputing,itsadvantages,andthefundamentalsofAWS.Thefollowingchapterswillguideyouonhowtomanagemultipleaccountsbysettingupconsolidatedbilling,enhancingyourapplicationdeliveryskills,withthelatestAWSservicessuchasCodeCommit,CodeDeploy,andCodePipelinetoprovidecontinuousdeliveryanddeployment,whilealsosecuringandmonitoringyourenvironment'sworkflow.It’llalsoaddtoyourunderstandingoftheservicesAWSLambdaprovidestodevelopers.Torefineyourskillsfurther,itdemonstrateshowtodesign,write,test,monitor,andtroubleshootLambdafunctions.BytheendofthisLearningPath,you’llbeabletocreateahighlysecure,fault-tolerant,andscalableenvironmentforyourapplications.ThisLearningPathincludescontentfromthefollowingPacktproducts:AWSAdministration:TheDefinitiveGuide,SecondEditionbyYohanWadia.AWSAdministrationCookbookbyRowanUdell,LucasChan.MasteringAWSLambdabyYohanWadia,UditaGupta.

Yohan Wadia Rowan Udell Lucas Chan Udita Gupta ·自動化 ·12.8萬字

Azure for Architects
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Azure for Architects

Overtheyears,Azurecloudserviceshavegrownquickly,andthenumberoforganizationsadoptingAzurefortheircloudservicesisalsograduallyincreasing.LeadingindustrygiantsarefindingthatAzurefulfillstheirextensivecloudrequirements.AzureforArchitects–SecondEditionstartswithanextensiveintroductiontomajordesigningandarchitecturalaspectsavailablewithAzure.Thesedesignpatternsfocusondifferentaspectsofthecloud,suchashighavailability,security,andscalability.Gradually,wemoveontootheraspects,suchasARMtemplatemodulardesignanddeployments.Thisistheageofmicroservicesandserverlessisthepreferredimplementationmechanismforthem.ThisbookcoverstheentireserverlessstackavailableinAzureincludingAzureEventGrid,AzureFunctions,andAzureLogicApps.Newandadvancefeatureslikedurablefunctionsarediscussedatlength.Acompleteintegrationsolutionusingtheseserverlesstechnologiesisalsopartofthebook.AcompletechapterdiscussesallpossibleoptionsrelatedtocontainersinAzureincludingAzureKubernetesservices,AzureContainerInstancesandRegistry,andWebAppforContainers.DatamanagementandintegrationisanintegralpartofthisbookthatdiscussesoptionsforimplementingOLTPsolutionsusingAzureSQL,BigDatasolutionsusingAzureDatafactoryandDataLakeStorage,eventingsolutionsusingstreamanalytics,andEventHubs.ThisbookwillprovideinsightsintoAzuregovernancefeaturessuchastagging,RBAC,costmanagement,andpolicies.Bytheendofthisbook,youwillbeabletodevelopafull-?edgedAzurecloudsolutionthatisEnterpriseclassandfuture-ready.

Ritesh Modi ·自動化 ·8.6萬字

Generative Adversarial Networks Projects
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Generative Adversarial Networks Projects

GenerativeAdversarialNetworks(GANs)havethepotentialtobuildnext-generationmodels,astheycanmimicanydistributionofdata.Majorresearchanddevelopmentworkisbeingundertakeninthisfieldsinceitisoneoftherapidlygrowingareasofmachinelearning.Thisbookwilltestunsupervisedtechniquesfortrainingneuralnetworksasyoubuildsevenend-to-endprojectsintheGANdomain.GenerativeAdversarialNetworkProjectsbeginsbycoveringtheconcepts,tools,andlibrariesthatyouwillusetobuildefficientprojects.Youwillalsouseavarietyofdatasetsforthedifferentprojectscoveredinthebook.Thelevelofcomplexityoftheoperationsrequiredincreaseswitheverychapter,helpingyougettogripswithusingGANs.Youwillcoverpopularapproachessuchas3D-GAN,DCGAN,StackGAN,andCycleGAN,andyou’llgainanunderstandingofthearchitectureandfunctioningofgenerativemodelsthroughtheirpracticalimplementation.Bytheendofthisbook,youwillbereadytobuild,train,andoptimizeyourownend-to-endGANmodelsatworkorinyourownprojects.

Kailash Ahirwar ·自動化 ·5.2萬字

Ensemble Machine Learning Cookbook
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Ensemble Machine Learning Cookbook

Ensemblemodelingisanapproachusedtoimprovetheperformanceofmachinelearningmodels.Itcombinestwoormoresimilarordissimilarmachinelearningalgorithmstodeliversuperiorintellectualpowers.Thisbookwillhelpyoutoimplementpopularmachinelearningalgorithmstocoverdifferentparadigmsofensemblemachinelearningsuchasboosting,bagging,andstacking.TheEnsembleMachineLearningCookbookwillstartbygettingyouacquaintedwiththebasicsofensembletechniquesandexploratorydataanalysis.You'llthenlearntoimplementtasksrelatedtostatisticalandmachinelearningalgorithmstounderstandtheensembleofmultipleheterogeneousalgorithms.Itwillalsoensurethatyoudon'tmissoutonkeytopics,suchaslikeresamplingmethods.Asyouprogress,you’llgetabetterunderstandingofbagging,boosting,stacking,andworkingwiththeRandomForestalgorithmusingreal-worldexamples.Thebookwillhighlighthowtheseensemblemethodsusemultiplemodelstoimprovemachinelearningresults,ascomparedtoasinglemodel.Intheconcludingchapters,you'lldelveintoadvancedensemblemodelsusingneuralnetworks,naturallanguageprocessing,andmore.You’llalsobeabletoimplementmodelssuchasfrauddetection,textcategorization,andsentimentanalysis.Bytheendofthisbook,you'llbeabletoharnessensembletechniquesandtheworkingmechanismsofmachinelearningalgorithmstobuildintelligentmodelsusingindividualrecipes.

Dipayan Sarkar Vijayalakshmi Natarajan ·自動化 ·5.5萬字

Ceph：Designing and Implementing Scalable Storage Systems
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Ceph：Designing and Implementing Scalable Storage Systems

ThisLearningPathtakesyouthroughthebasicsofCephallthewaytogainingin-depthunderstandingofitsadvancedfeatures.You’llgatherskillstoplan,deploy,andmanageyourCephcluster.AfteranintroductiontotheCepharchitectureanditscoreprojects,you’llbeabletosetupaCephclusterandlearnhowtomonitoritshealth,improveitsperformance,andtroubleshootanyissues.Byfollowingthestep-by-stepapproachofthisLearningPath,you’lllearnhowCephintegrateswithOpenStack,Glance,Manila,Swift,andCinder.WithknowledgeoffederatedarchitectureandCephFS,you’lluseCalamariandVSMtomonitortheCephenvironment.Intheupcomingchapters,you’llstudythekeyareasofCeph,includingBlueStore,erasurecoding,andcachetiering.Morespecifically,you’lldiscoverwhattheycandoforyourstoragesystem.Intheconcludingchapters,youwilldevelopapplicationsthatuseLibradosanddistributedcomputationswithsharedobjectclasses,andseehowCephanditssupportinginfrastructurecanbeoptimized.BytheendofthisLearningPath,you'llhavethepracticalknowledgeofoperatingCephinaproductionenvironment.ThisLearningPathincludescontentfromthefollowingPacktproducts:CephCookbookbyMichaelHackett,VikhyatUmraoandKaranSingh.MasteringCephbyNickFisk.LearningCeph,SecondEditionbyAnthonyD'Atri,VaibhavBhembreandKaranSingh.

Michael Hackett Vikhyat Umrao Karan Singh Nick Fisk Anthony D'Atri Vaibhav Bhembre ·自動化 ·10.8萬字

Mastering Hadoop 3
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Mastering Hadoop 3

ApacheHadoopisoneofthemostpopularbigdatasolutionsfordistributedstorageandforprocessinglargechunksofdata.WithHadoop3,Apachepromisestoprovideahigh-performance,morefault-tolerant,andhighlyefficientbigdataprocessingplatform,withafocusonimprovedscalabilityandincreasedefficiency.Withthisguide,you’llunderstandadvancedconceptsoftheHadoopecosystemtool.You’lllearnhowHadoopworksinternally,studyadvancedconceptsofdifferentecosystemtools,discoversolutionstoreal-worldusecases,andunderstandhowtosecureyourcluster.ItwillthenwalkyouthroughHDFS,YARN,MapReduce,andHadoop3concepts.You’llbeabletoaddresscommonchallengeslikeusingKafkaefficiently,designinglowlatency,reliablemessagedeliveryKafkasystems,andhandlinghighdatavolumes.Asyouadvance,you’lldiscoverhowtoaddressmajorchallengeswhenbuildinganenterprise-grademessagingsystem,andhowtousedifferentstreamprocessingsystemsalongwithKafkatofulfilyourenterprisegoals.Bytheendofthisbook,you’llhaveacompleteunderstandingofhowcomponentsintheHadoopecosystemareeffectivelyintegratedtoimplementafastandreliabledatapipeline,andyou’llbeequippedtotacklearangeofreal-worldproblemsindatapipelines.

Chanchal Singh Manish Kumar ·自動化 ·12.1萬字

Hands-On Business Intelligence with Qlik Sense
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Hands-On Business Intelligence with Qlik Sense

QlikSenseallowsyoutoexploresimple-to-complexdatatorevealhiddeninsightsanddatarelationshipstomakebusiness-drivendecisions.Hands-OnBusinessIntelligencewithQlikSensebeginsbyhelpingyougettogripswithunderlyingQlikconceptsandgivesyouanoverviewofallQlikSense’sfeatures.Youwilllearnadvancedmodelingtechniquesandlearnhowtoanalyzethedataloadedusingavarietyofvisualizationobjects.You’llalsobetrainedonhowtoshareappsthroughQlikSenseEnterpriseandQlikSenseCloudandhowtoperformaggregationwithAGGR.Asyouprogressthroughthechapters,you’llexplorethestoriesfeaturetocreatedata-drivenpresentationsandupdateanexistingstory.ThisbookwillguideyouthroughtheGeoAnalyticsfeaturewiththegeo-mappingobjectandGeoAnalyticsconnector.Furthermore,you’lllearnabouttheself-serviceanalyticsfeaturesandperformdataforecastingusingadvancedanalytics.Lastly,you’lldeployQlikSenseappsformobileandtablet.Bytheendofthisbook,youwillbewell-equippedtorunsuccessfulbusinessintelligenceapplicationsusingQlikSense'sfunctionality,datamodelingtechniques,andvisualizationbestpractices.

Pablo Labbe Clever Anjos Kaushik Solanki Jerry DiMaso ·自動化 ·5.4萬字

Installation，Storage，and Compute with Windows Server 2016：Microsoft 70-740 MCSA Exam Guide
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Installation，Storage，and Compute with Windows Server 2016：Microsoft 70-740 MCSA Exam Guide

MCSA:WindowsServer2016certificationisoneofthemostsought-aftercertificationsforITprofessionals,whichincludesworkingwithWindowsServerandperformingadministrativetasksaroundit.Thisbookisaimedatthe70-740certificationandispartofPackt'sthree-bookseriesonMCSAWindowsServer2016certification,whichcoversExam70-740,Exam70-741,andExam70-742.Thisbookwillcoverexamobjectivesforthe70-740exam,andstartingfrominstallingandconfiguringWindowsServer2016,WindowsServerimaginganddeploymenttoconfiguringandmanagingdisksandvolumes,implementingandconfiguringserverstorageandimplementingHyper-V.Attheendofeachchapteryouwillbeprovidedtestquestionstoreviseyourlearningswhichwillboostyourconfidenceinpreparingfortheactualcertifications.Bytheendofthisbook,youwilllearneverythingneededtopassthe,MCSAExam70-740:Installation,Storage,andComputewithWindowsServer2016,certification.

Sasha Kranjac Vladimir Stefanovic ·自動化 ·5.2萬字

Python Machine Learning By Example
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Python Machine Learning By Example

Thesurgeininterestinmachinelearning(ML)isduetothefactthatitrevolutionizesautomationbylearningpatternsindataandusingthemtomakepredictionsanddecisions.Ifyou’reinterestedinML,thisbookwillserveasyourentrypointtoML.PythonMachineLearningByExamplebeginswithanintroductiontoimportantMLconceptsandimplementationsusingPythonlibraries.Eachchapterofthebookwalksyouthroughanindustryadoptedapplication.You’llimplementMLtechniquesinareassuchasexploratorydataanalysis,featureengineering,andnaturallanguageprocessing(NLP)inaclearandeasy-to-followway.Withthehelpofthisextendedandupdatededition,you’llunderstandhowtotackledata-drivenproblemsandimplementyoursolutionswiththepowerfulyetsimplePythonlanguageandpopularPythonpackagesandtoolssuchasTensorFlow,scikit-learn,gensim,andKeras.ToaidyourunderstandingofpopularMLalgorithms,thebookcoversinterestingandeasy-to-followexamplessuchasnewstopicmodelingandclassification,spamemaildetection,stockpriceforecasting,andmore.Bytheendofthebook,you’llhaveputtogetherabroadpictureoftheMLecosystemandwillbewell-versedwiththebestpracticesofapplyingMLtechniquestomakethemostoutofnewopportunities.

Yuxi (Hayden) Liu ·自動化 ·7.7萬字

Mastering Docker Enterprise
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Mastering Docker Enterprise

Whileknownmostlyastheopensourceenginebehindtensofmillionsofservernodes,DockeralsoofferscommerciallysupportedenterprisetoolingknownastheDockerEnterprise.ThisplatformleveragesthedeeprootsfromDockerEngine-Community(formerlyDockerCE)andKubernetes,butaddssupportandtoolingtoefficientlyoperateasecurecontainerplatformatscale.Withhundredsofenterprisesonboard,bestpracticesandadoptionpatternsareemergingrapidly.Theselearningpointscanbeusedtoinformadoptersandhelpmanagetheenterprisetransformationassociatedwithenterprisecontaineradoption.ThisbookstartsbyexplainingthecaseforDockerEnterprise,aswellasitsstructureandreferencearchitecture.Fromthere,weprogressthroughthePoC,pilotandproductionstagesasaworkingmodelforadoption,evolvingtheplatform’sdesignandconfigurationforeachstageandusingdetailedapplicationexamplesalongthewaytoclarifyanddemonstrateimportantconcepts.ThebookconcludeswithDocker’simpactonotheremergingsoftwaretechnologies,suchasBlockchainandServerlesscomputing.Bytheendofthisbook,you’llhaveabetterunderstandingofwhatittakestogetyourenterpriseupandrunningwithDockerEnterpriseandbeyond.

Mark Panthofer ·自動化 ·10.9萬字

Building Google Cloud Platform Solutions
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Building Google Cloud Platform Solutions

GCPisacloudcomputingplatformwithawiderangeofproductsandservicesthatenableyoutobuildanddeploycloud-hostedapplications.ThisLearningPathwillguideyouinusingGCPanddesigning,deploying,andmanagingapplicationsonGoogleCloud.YouwillgetstartedbylearninghowtouseAppEnginetoaccessGoogle'sscalablehostingandbuildsoftwarethatrunsonthisframework.WiththehelpofGoogleComputeEngine,you’llbeabletohostyourworkloadonvirtualmachineinstances.Thelaterchapterswillhelpyoutoexplorewaystoimplementauthenticationandsecurity,CloudAPIs,andcommand-lineanddeploymentmanagement.Asyouhoneyourskills,you’llunderstandhowtointegrateyournewapplicationswithvariousdatasolutionsonGCP,includingCloudSQL,Bigtable,andCloudStorage.Followingthis,thebookwillteachyouhowtostreamlineyourworkflowwithtools,includingSourceRepositories,ContainerBuilder,andStackdriver.You'llalsounderstandhowtodeployanddebugserviceswithIntelliJ,implementcontinuousdeliverypipelines,andconfigurerobustmonitoringandalertsforyourproductionsystems.BytheendofthisLearningPath,you'llbewellversedwithGCP’sdevelopmenttoolsandbeabletodevelop,deploy,andmanagehighlyscalableandreliableapplications.ThisLearningPathincludescontentfromthefollowingPacktproducts:GoogleCloudPlatformforDevelopersTedHunterandStevenPorter.GoogleCloudPlatformCookbookbyLegorieRajanPS.

Ted Hunter Steven Porter Legorie Rajan PS ·自動化 ·16.2萬字

Getting Started with Containerization
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Getting Started with Containerization

Kubernetesisanopensourceorchestrationplatformformanagingcontainersinaclusterenvironment.ThisLearningPathintroducesyoutotheworldofcontainerization,inadditiontoprovidingyouwithanoverviewofDockerfundamentals.Asyouprogress,youwillbeabletounderstandhowKubernetesworkswithcontainers.StartingwithcreatingKubernetesclustersandrunningapplicationswithproperauthenticationandauthorization,you'lllearnhowtocreatehigh-availabilityKubernetesclustersonAmazonWebServices(AWS),andalsolearnhowtousekubeconfigtomanagedifferentclusters.WhetheritislearningaboutDockercontainersandDockerCompose,orbuildingacontinuousdeliverypipelineforyourapplication,thisLearningPathwillequipyouwithalltherighttoolsandtechniquestogetstartedwithcontainerization.BytheendofthisLearningPath,youwillhavegainedhands-onexperienceofworkingwithDockercontainersandorchestrators,includingSwarmKitandKubernetes.ThisLearningPathincludescontentfromthefollowingPacktproducts:KubernetesCookbook-SecondEditionbyHidetoSaito,Hui-ChuanChloeLee,andKe-JouCarolHsu.LearnDocker-FundamentalsofDocker18.xbyGabrielN.Schenker.

Gabriel N. Schenker Hideto Saito Hui Chuan Chloe Lee Ke Jou Carol Hsu ·自動化 ·13.9萬字

Hands-On Neural Networks with Keras
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Hands-On Neural Networks with Keras

NeuralnetworksareusedtosolveawiderangeofproblemsindifferentareasofAIanddeeplearning.Hands-OnNeuralNetworkswithKeraswillstartwithteachingyouaboutthecoreconceptsofneuralnetworks.Youwilldelveintocombiningdifferentneuralnetworkmodelsandworkwithreal-worldusecases,includingcomputervision,naturallanguageunderstanding,syntheticdatageneration,andmanymore.Movingon,youwillbecomewellversedwithconvolutionalneuralnetworks(CNNs),recurrentneuralnetworks(RNNs),longshort-termmemory(LSTM)networks,autoencoders,andgenerativeadversarialnetworks(GANs)usingreal-worldtrainingdatasets.WewillexaminehowtouseCNNsforimagerecognition,howtousereinforcementlearningagents,andmanymore.Wewilldiveintothespecificarchitecturesofvariousnetworksandthenimplementeachoftheminahands-onmannerusingindustry-gradeframeworks.Bytheendofthisbook,youwillbehighlyfamiliarwithallprominentdeeplearningmodelsandframeworks,andtheoptionsyouhavewhenapplyingdeeplearningtoreal-worldscenariosandembeddingartificialintelligenceasthecorefabricofyourorganization.

Niloy Purkait ·自動化 ·11.4萬字

Big Data Analysis with Python
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Big Data Analysis with Python

Processingbigdatainrealtimeischallengingduetoscalability,informationinconsistency,andfaulttolerance.BigDataAnalysiswithPythonteachesyouhowtousetoolsthatcancontrolthisdataavalancheforyou.Withthisbook,you'lllearnpracticaltechniquestoaggregatedataintousefuldimensionsforposterioranalysis,extractstatisticalmeasurements,andtransformdatasetsintofeaturesforothersystems.ThebookbeginswithanintroductiontodatamanipulationinPythonusingpandas.You'llthengetfamiliarwithstatisticalanalysisandplottingtechniques.Withmultiplehands-onactivitiesinstore,you'llbeabletoanalyzedatathatisdistributedonseveralcomputersbyusingDask.Asyouprogress,you'llstudyhowtoaggregatedataforplotswhentheentiredatacannotbeaccommodatedinmemory.You'llalsoexploreHadoop(HDFSandYARN),whichwillhelpyoutacklelargerdatasets.ThebookalsocoversSparkandexplainshowitinteractswithothertools.Bytheendofthisbook,you'llbeabletobootstrapyourownPythonenvironment,processlargefiles,andmanipulatedatatogeneratestatistics,metrics,andgraphs.

Ivan Marin Ankit Shukla Sarang VK ·自動化 ·4萬字

Python Data Mining Quick Start Guide
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Python Data Mining Quick Start Guide

Dataminingisanecessaryandpredictableresponsetothedawnoftheinformationage.Itistypicallydefinedasthepatternand/ortrenddiscoveryphaseinthedataminingpipeline,andPythonisapopulartoolforperformingthesetasksasitoffersawidevarietyoftoolsfordatamining.ThisbookwillserveasaquickintroductiontotheconceptofdataminingandputtingittopracticalusewiththehelpofpopularPythonpackagesandlibraries.Youwillgetahands-ondemonstrationofworkingwithdifferentreal-worlddatasetsandextractingusefulinsightsfromthemusingpopularPythonlibrariessuchasNumPy,pandas,scikit-learn,andmatplotlib.Youwillthenlearnthedifferentstagesofdataminingsuchasdataloading,cleaning,analysis,andvisualization.Youwillalsogetafullconceptualdescriptionofpopulardatatransformation,clustering,andclassificationtechniques.Bytheendofthisbook,youwillbeabletobuildanefficientdataminingpipelineusingPythonwithoutanyhassle.

Nathan Greeneltch ·自動化 ·3.5萬字

Network Science with Python and NetworkX Quick Start Guide
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Network Science with Python and NetworkX Quick Start Guide

NetworkXisaleadingfreeandopensourcepackageusedfornetworksciencewiththePythonprogramminglanguage.NetworkXcantrackpropertiesofindividualsandrelationships,findcommunities,analyzeresilience,detectkeynetworklocations,andperformawiderangeofimportanttasks.Withtherecentreleaseofversion2,NetworkXhasbeenupdatedtobemorepowerfulandeasytouse.Ifyou’readatascientist,engineer,orcomputationalsocialscientist,thisbookwillguideyouinusingthePythonprogramminglanguagetogaininsightsintoreal-worldnetworks.Startingwiththefundamentals,you’llbeintroducedtothecoreconceptsofnetworkscience,alongwithexamplesthatusereal-worlddataandPythoncode.Thisbookwillintroduceyoutotheoreticalconceptssuchasscale-freeandsmall-worldnetworks,centralitymeasures,andagent-basedmodeling.You’llalsobeabletolookforscale-freenetworksinrealdataandvisualizeanetworkusingcircular,directed,andshelllayouts.Bytheendofthisbook,you’llbeabletochooseappropriatenetworkrepresentations,useNetworkXtobuildandcharacterizenetworks,anduncoverinsightswhileworkingwithreal-worldsystems.

Edward L. Platt ·自動化 ·3.7萬字

QQ閱讀手機版

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