A Comprehensive Guide To Types Of Neural Networks

What are the Different Types of Neural Networks? · 1. Feedforward Neural Network – Artificial Neuron · 2. Radial Basis Function Neural Network · 3. Multilayer ... AComprehensiveGuidetoTypesofNeuralNetworks Muchofmoderntechnologyisbasedoncomputationalmodelsknownasartificialneuralnetworks.Therearemanydifferenttypesofneuralnetworkswhichfunctiononthesameprinciplesasthenervoussysteminthehumanbody. Whatisaneuralnetwork? AsHowardRheingoldsaid,“Theneuralnetworkisthiskindoftechnologythatisnotanalgorithm,itisanetworkthathasweightsonit,andyoucanadjusttheweightssothatitlearns.Youteachitthroughtrials.”Bythis,youwouldbeclearwithneuralnetworkdefinition. WhatareArtificialNeuralNetworks?TableofContentsWhatareArtificialNeuralNetworks?HowdoNeuralNetworkswork?WhataretheDifferentTypesofNeuralNetworks?1.FeedforwardNeuralNetwork–ArtificialNeuron2.RadialBasisFunctionNeuralNetwork3.MultilayerPerceptron4.ConvolutionalNeuralNetwork5.RecurrentNeuralNetwork(RNN)–LongShortTermMemory6.ModularNeuralNetwork7.Sequence-To-SequenceModelsSummingup Anartificialneuralnetworkisasystemofhardwareorsoftwarethatispatternedaftertheworkingofneuronsinthehumanbrainandnervoussystem.ArtificialneuralnetworksareavarietyofdeeplearningtechnologywhichcomesunderthebroaddomainofArtificialIntelligence. DeeplearningisabranchofMachineLearningwhichusesdifferenttypesofneuralnetworks.ThesealgorithmsareinspiredbythewayourbrainfunctionsandthereforemanyexpertsbelievetheyareourbestshottomovingtowardsrealAI(ArtificialIntelligence). RegisterForaFreeWebinarDate:19thMar,2022(Saturday)Time:11:00AMto12:00PM(IST/GMT+5:30) HiddenWhichProgramareyouinterestedin?*DigitalMarketingDataScienceExcelwithPowerBIIamnotclear.ArrangeasessionwithcareercounsellorName*Email* Phone*Registerme RegistermeforFREEOrientationSession Course* Sendmecoursecurriculumaswell Termandcondition* IagreetoDigitalVidyaPrivacyPolicy&TermsofUse. NameThisfieldisforvalidationpurposesandshouldbeleftunchanged. Δ Deeplearningisbecomingespeciallyexcitingnowaswehavemoreamountsofdataandlargerneuralnetworkstoworkwith. Moreover,theperformanceofneuralnetworksimprovesastheygrowbiggerandworkwithmoreandmoredata,unlikeotherMachineLearningalgorithmswhichcanreachaplateauafterapoint. Neuralnetworks HowdoNeuralNetworkswork? Aneuralnetworkhasalargenumberofprocessors.Theseprocessorsoperateparallellybutarearrangedastiers.Thefirsttierreceivestherawinputsimilartohowtheopticnervereceivestherawinformationinhumanbeings. Eachsuccessivetierthenreceivesinputfromthetierbeforeitandthenpassesonitsoutputtothetierafterit.Thelasttierprocessesthefinaloutput. Smallnodesmakeupeachtier.Thenodesarehighlyinterconnectedwiththenodesinthetierbeforeandafter.Eachnodeintheneuralnetworkhasitsownsphereofknowledge,includingrulesthatitwasprogrammedwithandrulesithaslearntbyitself.  Thekeytotheefficacyofneuralnetworksistheyareextremelyadaptiveandlearnveryquickly.Eachnodeweighstheimportanceoftheinputitreceivesfromthenodesbeforeit.Theinputsthatcontributethemosttowardstherightoutputaregiventhehighestweight. WhataretheDifferentTypesofNeuralNetworks? Differenttypesofneuralnetworksusedifferentprinciplesindeterminingtheirownrules.Therearemanytypesofartificialneuralnetworks,eachwiththeiruniquestrengths.Youcantakealookatthis videotoseethedifferenttypesofneuralnetworksandtheirapplicationsindetail. Herearesomeofthemostimportanttypesofneuralnetworksandtheirapplications. 1.FeedforwardNeuralNetwork–ArtificialNeuron Thisisoneofthesimplesttypesofartificialneuralnetworks.Inafeedforwardneuralnetwork,thedatapassesthroughthedifferentinputnodesuntilitreachestheoutputnode. Inotherwords,datamovesinonlyonedirectionfromthefirsttieronwardsuntilitreachestheoutputnode.Thisisalsoknownasafrontpropagatedwavewhichisusuallyachievedbyusingaclassifyingactivationfunction. Unlikeinmorecomplextypesofneuralnetworks,thereisnobackpropagationanddatamovesinonedirectiononly.Afeedforwardneuralnetworkmayhaveasinglelayeroritmayhavehiddenlayers. Inafeedforwardneuralnetwork,thesumoftheproductsoftheinputsandtheirweightsarecalculated.Thisisthenfedtotheoutput.Hereisanexampleofasinglelayerfeedforwardneuralnetwork. Feedforwardneuralnetwork–artificialneuron Feedforwardneuralnetworksareusedintechnologieslikefacerecognitionandcomputervision.Thisisbecausethetargetclassesintheseapplicationsarehardtoclassify. Asimplefeedforwardneuralnetworkisequippedtodealwithdatawhichcontainsalotofnoise.Feedforwardneuralnetworksarealsorelativelysimpletomaintain. RegisterForaFreeWebinarDate:19thMar,2022(Saturday)Time:11:00AMto12:00PM(IST/GMT+5:30) HiddenWhichProgramareyouinterestedin?*DigitalMarketingDataScienceExcelwithPowerBIIamnotclear.ArrangeasessionwithcareercounsellorName*Email* Phone*Registerme RegistermeforFREEOrientationSession Course* Sendmecoursecurriculumaswell Termandcondition* IagreetoDigitalVidyaPrivacyPolicy&TermsofUse. NameThisfieldisforvalidationpurposesandshouldbeleftunchanged. Δ 2.RadialBasisFunctionNeuralNetwork Aradialbasisfunctionconsidersthedistanceofanypointrelativetothecentre.Suchneuralnetworkshavetwolayers.Intheinnerlayer,thefeaturesarecombinedwiththeradialbasisfunction. Thentheoutputofthesefeaturesistakenintoaccountwhencalculatingthesameoutputinthenexttime-step.Hereisadiagramwhichrepresentsaradialbasisfunctionneuralnetwork. Radialbasisfunctionneuralnetwork Theradialbasisfunctionneuralnetworkisappliedextensivelyinpowerrestorationsystems.Inrecentdecades,powersystemshavebecomebiggerandmorecomplex. Thisincreasestheriskofablackout.Thisneuralnetworkisusedinthepowerrestorationsystemsinordertorestorepowerintheshortestpossibletime. 3.MultilayerPerceptron Amultilayerperceptronhasthreeormorelayers.Itisusedtoclassifydatathatcannotbeseparatedlinearly.Itisatypeofartificialneuralnetworkthatisfullyconnected.Thisisbecauseeverysinglenodeinalayerisconnectedtoeachnodeinthefollowinglayer. Amultilayerperceptronusesanonlinearactivationfunction(mainlyhyperbolictangentorlogisticfunction).Here’swhatamultilayerperceptronlookslike. Multilayerperceptron Thistypeofneuralnetworkisappliedextensivelyinspeechrecognitionandmachinetranslationtechnologies. 4.ConvolutionalNeuralNetwork Aconvolutionalneuralnetwork(CNN)usesavariationofthemultilayerperceptrons.ACNNcontainsoneormorethanoneconvolutionallayers.Theselayerscaneitherbecompletelyinterconnectedorpooled. Beforepassingtheresulttothenextlayer,theconvolutionallayerusesaconvolutionaloperationontheinput.Duetothisconvolutionaloperation,thenetworkcanbemuchdeeperbutwithmuchfewerparameters. Duetothisability,convolutionalneuralnetworksshowveryeffectiveresultsinimageandvideorecognition,naturallanguageprocessing,andrecommendersystems. Convolutionalneuralnetworksalsoshowgreatresultsinsemanticparsingandparaphrasedetection.Theyarealsoappliedinsignalprocessingandimageclassification. CNN’sarealsobeingusedinimageanalysisandrecognitioninagriculturewhereweatherfeaturesareextractedfromsatelliteslikeLSATtopredictthegrowthandyieldofapieceofland.Here’sanimageofwhataConvolutionalNeuralNetworklookslike. Convolutionalneuralnetwork 5.RecurrentNeuralNetwork(RNN)–LongShortTermMemory ARecurrentNeuralNetworkisatypeofartificialneuralnetworkinwhichtheoutputofaparticularlayerissavedandfedbacktotheinput.Thishelpspredicttheoutcomeofthelayer. Thefirstlayerisformedinthesamewayasitisinthefeedforwardnetwork.Thatis,withtheproductofthesumoftheweightsandfeatures.However,insubsequentlayers,therecurrentneuralnetworkprocessbegins. Fromeachtime-steptothenext,eachnodewillremembersomeinformationthatithadintheprevioustime-step.Inotherwords,eachnodeactsasamemorycellwhilecomputingandcarryingoutoperations.Theneuralnetworkbeginswiththefrontpropagationasusualbutrememberstheinformationitmayneedtouselater. Ifthepredictioniswrong,thesystemself-learnsandworkstowardsmakingtherightpredictionduringthebackpropagation.Thistypeofneuralnetworkisveryeffectiveintext-to-speechconversiontechnology. Here’swhatarecurrentneuralnetworklookslike. Recurrentneuralnetwork(rnn)–longshorttermmemory 6.ModularNeuralNetwork Amodularneuralnetworkhasanumberofdifferentnetworksthatfunctionindependentlyandperformsub-tasks.Thedifferentnetworksdonotreallyinteractwithorsignaleachotherduringthecomputationprocess.Theyworkindependentlytowardsachievingtheoutput. Asaresult,alargeandcomplexcomputationalprocesscanbedonesignificantlyfasterbybreakingitdownintoindependentcomponents.Thecomputationspeedincreasesbecausethenetworksarenotinteractingwithorevenconnectedtoeachother. Here’savisualrepresentationofaModularNeuralNetwork. Modularneuralnetwork 7.Sequence-To-SequenceModels Asequencetosequencemodelconsistsoftworecurrentneuralnetworks.There’sanencoderthatprocessestheinputandadecoderthatprocessestheoutput.Theencoderanddecodercaneitherusethesameordifferentparameters.Thismodelisparticularlyapplicableinthosecaseswherethelengthoftheinputdataisnotthesameasthelengthoftheoutputdata.  Sequence-to-sequencemodelsareappliedmainlyinchatbots,machinetranslation,andquestionansweringsystems. RegisterForaFreeWebinarDate:19thMar,2022(Saturday)Time:11:00AMto12:00PM(IST/GMT+5:30) HiddenWhichProgramareyouinterestedin?*DigitalMarketingDataScienceExcelwithPowerBIIamnotclear.ArrangeasessionwithcareercounsellorName*Email* Phone*Registerme RegistermeforFREEOrientationSession Course* Sendmecoursecurriculumaswell Termandcondition* IagreetoDigitalVidyaPrivacyPolicy&TermsofUse. CommentsThisfieldisforvalidationpurposesandshouldbeleftunchanged. Δ Summingup Therearemanytypesofartificialneuralnetworksthatoperateindifferentwaystoachievedifferentoutcomes.Themostimportantpartaboutneuralnetworksisthattheyaredesignedinawaythatissimilartohowneuronsinthebrainwork. Asaresult,theyaredesignedtolearnmoreandimprovemorewithmoredataandmoreusage.Unliketraditionalmachinelearningalgorithmswhichtendtostagnateafteracertainpoint,neuralnetworkshavetheabilitytotrulygrowwithmoredataandmoreusage. That’swhymanyexpertsbelievethatdifferenttypesofneuralnetworkswillbethefundamentalframeworkonwhichnext-generationArtificialIntelligencewillbebuilt.ThustakingaMachineLearningCoursewillprovetobeanaddedbenefit. Hopefully,bynowyoumusthaveunderstoodtheconceptofNeuralNetworksanditstypes.Moreover,ifyouarealsoinspiredbytheopportunityofMachineLearning,enrolinourMachineLearningusingPythonCourse. AnukratiMehtaAcreativewriter,capableofcuratingengagingcontentinvariousdomainsincludingtechnicalarticles,marketingcopy,websitecontent,andPR. 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