An Ultimate Tutorial to Neural Networks in 2022 - Simplilearn

Let us begin this Neural Network tutorial by understanding: “What is a ... different information in each, which makes it powerful and fast. AI&MachineLearningDataScience&BusinessAnalyticsAI&MachineLearningProjectManagementCyberSecurityCloudComputingDevOpsBusinessandLeadershipQualityManagementSoftwareDevelopmentAgileandScrumITServiceandArchitectureDigitalMarketingBigDataCareerFast-trackEnterpriseOtherSegmentsVideoTutorialsArticlesEbooksLiveWebinarsOn-demandWebinarsFreePracticeTestsHomeResourcesAI&MachineLearningDeepLearningTutorialforBeginnersAnUltimateTutorialtoNeuralNetworksTutorialPlaylistDeepLearningTutorialforBeginnersOverview WhatisDeepLearningandHowDoesItWork[Explained] Lesson-1 TheBestIntroductiontoDeepLearning-AStepbyStepGuide Lesson-2 Top10DeepLearningApplicationsUsedAcrossIndustries Lesson-3 WhatisNeuralNetwork:Overview,Applications,andAdvantages Lesson-4 NeuralNetworksTutorial Lesson-5 Top8DeepLearningFrameworks Lesson-6 Top10DeepLearningAlgorithmsYouShouldKnowin2021 Lesson-7 AnIntroductionToDeepLearningWithPython Lesson-8 WhatisTensorflow:DeepLearningLibrariesandProgramElementsExplained Lesson-9 HowToInstallTensorFlowonUbuntu Lesson-10 WhatIsTensorFlow2.0?TheBestGuidetoUnderstandTensorFlow Lesson-11 TensorFlowTutorialforBeginners:YourGatewaytoBuildingMachineLearningModels Lesson-12 ConvolutionalNeuralNetworkTutorial Lesson-13 RecurrentNeuralNetwork(RNN)TutorialforBeginners Lesson-14 TheBestIntroductiontoWhatGANsAre Lesson-15 WhatIsKeras?TheBestIntroductoryGuidetoKeras Lesson-16 FrequentlyaskedDeepLearningInterviewQuestionsandAnswers Lesson-17 TheUltimateGuidetoBuildingPowerfulKerasImageClassificationModels Lesson-18 AnUltimateTutorialtoNeuralNetworksLesson5of18BySimplilearnLastupdatedonMar8,202266537PreviousNextTutorialPlaylistDeepLearningTutorialforBeginnersOverview WhatisDeepLearningandHowDoesItWork[Explained] Lesson-1 TheBestIntroductiontoDeepLearning-AStepbyStepGuide Lesson-2 Top10DeepLearningApplicationsUsedAcrossIndustries Lesson-3 WhatisNeuralNetwork:Overview,Applications,andAdvantages Lesson-4 NeuralNetworksTutorial Lesson-5 Top8DeepLearningFrameworks Lesson-6 Top10DeepLearningAlgorithmsYouShouldKnowin2021 Lesson-7 AnIntroductionToDeepLearningWithPython Lesson-8 WhatisTensorflow:DeepLearningLibrariesandProgramElementsExplained Lesson-9 HowToInstallTensorFlowonUbuntu Lesson-10 WhatIsTensorFlow2.0?TheBestGuidetoUnderstandTensorFlow Lesson-11 TensorFlowTutorialforBeginners:YourGatewaytoBuildingMachineLearningModels Lesson-12 ConvolutionalNeuralNetworkTutorial Lesson-13 RecurrentNeuralNetwork(RNN)TutorialforBeginners Lesson-14 TheBestIntroductiontoWhatGANsAre Lesson-15 WhatIsKeras?TheBestIntroductoryGuidetoKeras Lesson-16 FrequentlyaskedDeepLearningInterviewQuestionsandAnswers Lesson-17 TheUltimateGuidetoBuildingPowerfulKerasImageClassificationModels Lesson-18 TableofContentsViewMore Artificialintelligenceandmachinelearninghaven’tjustgrabbedheadlinesandmadeforblockbustermovies;they’repoisedtomakearealdifferenceinoureverydaylives,suchaswithself-drivingcarsandlife-savingmedicaldevices.Infact,accordingtoGlobalBigDataConference,AIis“completelyreshapinglifesciences,medicine,andhealthcare”andisalsotransformingvoice-activatedassistants,imagerecognition,andmanyotherpopulartechnologies. ArtificialIntelligenceisatermusedformachinesthatcaninterpretthedata,learnfromit,anduseittodosuchtasksthatwouldotherwisebeperformedbyhumans.MachineLearningisabranchofArtificialIntelligencethatfocusesmoreontrainingthemachinestolearnontheirownwithoutmuchsupervision. Whatisaneuralnetwork?Ifyouarenotfamiliarwiththeseterms,thenthisneuralnetworktutorialwillhelpgainabetterunderstandingoftheseconcepts. LetusbeginthisNeuralNetworktutorialbyunderstanding:“Whatisaneuralnetwork?” PostGraduatePrograminAIandMachineLearningInPartnershipwithPurdueUniversityExploreCourse WhatisaNeuralNetwork? You’veprobablyalreadybeenusingneuralnetworksonadailybasis.Whenyouaskyourmobileassistanttoperformasearchforyou—say,GoogleorSiriorAmazonWeb—oruseaself-drivingcar,theseareallneuralnetwork-driven.Computergamesalsouseneuralnetworksonthebackend,aspartofthegamesystemandhowitadjuststotheplayers,andsodomapapplications,inprocessingmapimagesandhelpingyoufindthequickestwaytogettoyourdestination. Aneuralnetworkisasystemorhardwarethatisdesignedtooperatelikeahumanbrain. Neuralnetworkscanperformthefollowingtasks: Translatetext Identifyfaces Recognizespeech Readhandwrittentext Controlrobots Andalotmore Letuscontinuethisneuralnetworktutorialbyunderstandinghowaneuralnetworkworks. WorkingofNeuralNetwork Aneuralnetworkisusuallydescribedashavingdifferentlayers.Thefirstlayeristheinputlayer,itpicksuptheinputsignalsandpassesthemtothenextlayer.Thenextlayerdoesallkindsofcalculationsandfeatureextractions—it’scalledthehiddenlayer.Often,therewillbemorethanonehiddenlayer.Andfinally,there’sanoutputlayer,whichdeliversthefinalresult. Let’stakethereal-lifeexampleofhowtrafficcamerasidentifylicenseplatesandspeedingvehiclesontheroad.Thepictureitselfis28by28pixels,andtheimageisfedasaninputtoidentifythelicenseplate.Eachneuronhasanumber,calledactivation,whichrepresentsthegrayscalevalueofthecorrespondingpixel,rangingfrom0to1—it’s1forawhitepixeland0forablackpixel.Eachneuronislitupwhenitsactivationiscloseto1. Pixelsintheformofarraysarefedintotheinputlayer.Ifyourimageisbiggerthan28by28pixels,youmustshrinkitdown,becauseyoucan’tchangethesizeoftheinputlayer.Inourexample,we’llnametheinputsasX1,X2,andX3.Eachofthoserepresentsoneofthepixelscomingin.Theinputlayerthenpassestheinputtothehiddenlayer.Theinterconnectionsareassignedweightsatrandom.Theweightsaremultipliedwiththeinputsignal,andabiasisaddedtoallofthem. Theweightedsumoftheinputsisfedasinputtotheactivationfunction,todecidewhichnodestofireforfeatureextraction.Asthesignalflowswithinthehiddenlayers,theweightedsumofinputsiscalculatedandisfedtotheactivationfunctionineachlayertodecidewhichnodestofire. MasterDeepLearning,MachineLearning,andotherprogramminglanguageswithArtificialIntelligenceEngineerMaster’sProgram. Herewe’lltakeadetourtoexaminetheneuralnetworkactivationfunction.Therearedifferenttypesofactivationfunctions. SigmoidFunction Thesigmoidfunctionisusedwhenthemodelispredictingprobability. ThresholdFunction Thethresholdfunctionisusedwhenyoudon’twanttoworryabouttheuncertaintyinthemiddle. ReLU(rectifiedlinearunit)Function TheReLU(rectifiedlinearunit)functiongivesthevaluebutsaysifit’sover1,thenitwilljustbe1,andifit’slessthan0,itwilljustbe0.TheReLUfunctionismostcommonlyusedthesedays.  FreeCourse:IntroductiontoNeuralNetworkLearntheFundamentalsofNeuralNetworkEnrollNow HyperbolicTangentFunction Thehyperbolictangentfunctionissimilartothesigmoidfunctionbuthasarangeof-1to1. Nowthatyouknowwhatanactivationfunctionis,let’sgetbacktotheneuralnetwork.Finally,themodelwillpredicttheoutcome,applyingasuitableapplicationfunctiontotheoutputlayer.Inourexamplewiththecarimage,opticalcharacterrecognition(OCR)isusedtoconvertitintotexttoidentifywhat’swrittenonthelicenseplate.Inourneuralnetworkexample,weshowonlythreedotscomingin,eighthiddenlayernodes,andoneoutput,butthere’sreallyahugeamountofinputandoutput. Errorintheoutputisback-propagatedthroughthenetworkandweightsareadjustedtominimizetheerrorrate.Thisiscalculatedbyacostfunction.Youkeepadjustingtheweightsuntiltheyfitallthedifferenttrainingmodelsyouputin. Theoutputisthencomparedwiththeoriginalresult,andmultipleiterationsaredoneformaximumaccuracy.Witheveryiteration,theweightateveryinterconnectionisadjustedbasedontheerror.Thatmathgetscomplicated,sowe’renotgoingtodiveintoithere.But,wewouldlookathowit’sbeingdonewhileexecutingthecodeforourusecase. Inthefollowingsectionoftheneuralnetworktutorial,letusexplorethetypesofneuralnetworks. TypesofNeuralNetworks Thedifferenttypesofneuralnetworksarediscussedbelow: Feed-forwardNeuralNetwork ThisisthesimplestformofANN(artificialneuralnetwork);datatravelsonlyinonedirection(inputtooutput).Thisistheexamplewejustlookedat.Whenyouactuallyuseit,it’sfast;whenyou’retrainingit,ittakesawhile.Almostallvisionandspeechrecognitionapplicationsusesomeformofthistypeofneuralnetwork. RadialBasisFunctionsNeuralNetwork Thismodelclassifiesthedatapointbasedonitsdistancefromacenterpoint.Ifyoudon’thavetrainingdata,forexample,you’llwanttogroupthingsandcreateacenterpoint.Thenetworklooksfordatapointsthataresimilartoeachotherandgroupsthem.Oneoftheapplicationsforthisispowerrestorationsystems. KohonenSelf-organizingNeuralNetwork Vectorsofrandominputareinputtoadiscretemapcomprisedofneurons.Vectorsarealsocalleddimensionsorplanes.Applicationsincludeusingittorecognizepatternsindatalikeamedicalanalysis. RecurrentNeuralNetwork Inthistype,thehiddenlayersavesitsoutputtobeusedforfutureprediction.Theoutputbecomespartofitsnewinput.Applicationsincludetext-to-speechconversion.  ConvolutionNeuralNetwork Inthistype,theinputfeaturesaretakeninbatches—asiftheypassthroughafilter.Thisallowsthenetworktorememberanimageinparts.Applicationsincludesignalandimageprocessing,suchasfacialrecognition. ModularNeuralNetwork Thisiscomposedofacollectionofdifferentneuralnetworksworkingtogethertogettheoutput.Thisiscutting-edgeandisstillintheresearchphase. Thenextsectionoftheneuralnetworktutorialdealswiththeuseofcasesofneuralnetworks. FreeDeepLearningforBeginnersCourseMastertheBasicsofDeepLearningEnrollNow NeuralNetwork-UseCase Let’susethesystemtotellthedifferencebetweenacatandadog.Ourproblemstatementisthatwewanttoclassifyphotosofcatsanddogsusinganeuralnetwork.Wehaveavarietyofdogsandcatsinoursampleimages,andjustsortingthemoutisprettyamazing! CodingLanguageandEnvironment WewillimplementourusecasebybuildinganeuralnetworkinPython(version3.6).We’regoingtostartbyimportingtherequiredpackagesusingKeras: Let’stalkabouttheenvironmentwe’reworkingon.YoucanvisittheofficialwebsiteofKerasandthefirstthingyou’llnoticeisthatKerasoperatesontopofTensorFlow,CNTKorTheano.TensorFlowisprobablyoneofthemostwidelyusedpackageswithKeras.  FREECourse:IntroductiontoAIMasterthefundamentalsandkeyconceptsofAIStartLearning Kerasisuser-friendlyandhasmodularityandextensibility.ItalsoworkswithPython,whichisimportantbecausealotofpeopleindatasciencenowusePython.Whenyou’reworkingwithKeras,youcanaddlayerafterlayerwiththedifferentinformationineach,whichmakesitpowerfulandfast. Sidenote:Here,we’reusingAnacondawithPythoninit,andwehavecreatedourownpackagecalledkeraspython36.Ifyou’redoingalotofexperimentingwithdifferentpackages,youprobablywanttocreateyourownenvironmentinthere.  IntheAnacondaNavigator,ourkeraspython36islistedunderEnvironments.FromtheHomemenu,wecanlaunchtheJupyterNotebook,makingsureweusetherightenvironmentthatwejustsetup.Youcanuseanykindofsetupeditoryou’recomfortablewithforwhatyou’redoing,butwe’reusingPythonandJupyterNotebookforourexample. Conclusion Sowe’vesuccessfullybuiltaneuralnetworkusingPythonthatcandistinguishbetweenphotosofacatandadog.Imaginealltheotherthingsyoucoulddistinguishandallthedifferentindustriesyoucoulddiveintowiththat.Whatanexcitingtimetoliveinwiththesetoolswegettoplaywith. Ifyouhaveanyquestionsabouttheneuralnetworktutorial,headovertoSimplilearn.Wecanalsosendyouazipfolderofthedatausedhere.Wanttolearnmoreaboutneuralnetworksandartificialintelligence?TakeSimplilearn’sIntroductiontoArtificialIntelligenceforbeginners.AlreadyinAIandwanttofurtheryourcareer?BecomeanArtificialIntelligenceEngineerthroughSimplilearn’sMastersProgram. FindourDeepLearningwithKerasandTensorFlowOnlineClassroomtrainingclassesintopcities:NameDatePlaceDeepLearningwithKerasandTensorFlow19Mar-10Apr2022,WeekendbatchYourCityViewDetailsDeepLearningwithKerasandTensorFlow2Apr-24Apr2022,WeekendbatchSingaporeViewDetailsDeepLearningwithKerasandTensorFlow16Apr-8May2022,WeekendbatchYourCityViewDetailsAbouttheAuthorSimplilearnSimplilearnisoneoftheworld’sleadingprovidersofonlinetrainingforDigitalMarketing,CloudComputing,ProjectManagement,DataScience,IT,SoftwareDevelopment,andmanyotheremergingtechnologies.ViewMoreRecommendedProgramsDeepLearningwithKerasandTensorFlow18796LearnersArtificialIntelligenceEngineer17174LearnersLifetimeAccess**Lifetimeaccesstohigh-quality,self-pacede-learningcontent.ExploreCategoryFindDeepLearningwithKerasandTensorFlowinthesecitiesDeepLearningCourse(withKeras&TensorFlow)inSingaporeRecommendedResourcesWhatIsKeras?TheBestIntroductoryGuidetoKerasVideoTutorialDeepLearningInterviewGuideEbookKerasvsTensorflowvsPytorch:UnderstandingtheMostPopularDeepLearningFrameworksArticleProgramPreview:CaltechCTMEBootcampsinDataScienceandAI/MLWebinarWhatIsTensorFlow2.0?TheBestGuidetoUnderstandTensorFlowVideoTutorialMachineLearningCareerGuide:AcompleteplaybooktobecomingaMachineLearningEngineerEbookprevNext DisclaimerPMP,PMI,PMBOK,CAPM,PgMP,PfMP,ACP,PBA,RMP,SP,andOPM3areregisteredmarksoftheProjectManagementInstitute,Inc.