Top 10 Deep Learning Algorithms You Should Know in 2022

Get to know the top 10 Deep Learning Algorithms with examples such as ✔️CNN, LSTM, RNN, GAN, ... Deep learning models make use of several algorithms. 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WhatisDeepLearning? Deeplearningusesartificialneuralnetworkstoperformsophisticatedcomputationsonlargeamountsofdata.Itisatypeofmachinelearningthatworksbasedonthestructureandfunctionofthehumanbrain.  Deeplearningalgorithmstrainmachinesbylearningfromexamples.Industriessuchashealthcare,eCommerce,entertainment,andadvertisingcommonlyusedeeplearning. DeepLearningCourse(withTensorFlow&Keras)MastertheDeepLearningConceptsandModelsViewCourse DefiningNeuralNetworks Aneuralnetworkisstructuredlikethehumanbrainandconsistsofartificialneurons,alsoknownasnodes.Thesenodesarestackednexttoeachotherinthreelayers: Theinputlayer  Thehiddenlayer(s) Theoutputlayer Dataprovideseachnodewithinformationintheformofinputs.Thenodemultipliestheinputswithrandomweights,calculatesthem,andaddsabias.Finally,nonlinearfunctions,alsoknownasactivationfunctions,areappliedtodeterminewhichneurontofire. HowDeepLearningAlgorithmsWork? Whiledeeplearningalgorithmsfeatureself-learningrepresentations,theydependuponANNsthatmirrorthewaythebraincomputesinformation.Duringthetrainingprocess,algorithmsuseunknownelementsintheinputdistributiontoextractfeatures,groupobjects,anddiscoverusefuldatapatterns.Muchliketrainingmachinesforself-learning,thisoccursatmultiplelevels,usingthealgorithmstobuildthemodels. Deeplearningmodelsmakeuseofseveralalgorithms.Whilenoonenetworkisconsideredperfect,somealgorithmsarebettersuitedtoperformspecifictasks.Tochoosetherightones,it’sgoodtogainasolidunderstandingofallprimaryalgorithms. TypesofAlgorithmsusedinDeepLearning Hereisthelistoftop10mostpopulardeeplearningalgorithms: ConvolutionalNeuralNetworks(CNNs) LongShortTermMemoryNetworks(LSTMs) RecurrentNeuralNetworks(RNNs) GenerativeAdversarialNetworks(GANs) RadialBasisFunctionNetworks(RBFNs) MultilayerPerceptrons(MLPs) SelfOrganizingMaps(SOMs) DeepBeliefNetworks(DBNs) RestrictedBoltzmannMachines(RBMs) Autoencoders Deeplearningalgorithmsworkwithalmostanykindofdataandrequirelargeamountsofcomputingpowerandinformationtosolvecomplicatedissues.Now,letus,deep-dive,intothetop10deeplearningalgorithms. 1.ConvolutionalNeuralNetworks(CNNs) CNN's,alsoknownasConvNets,consistofmultiplelayersandaremainlyusedforimageprocessingandobjectdetection.YannLeCundevelopedthefirstCNNin1988whenitwascalledLeNet.ItwasusedforrecognizingcharacterslikeZIPcodesanddigits. FreeDeepLearningforBeginnersCourseMastertheBasicsofDeepLearningEnrollNow CNN'sarewidelyusedtoidentifysatelliteimages,processmedicalimages,forecasttimeseries,anddetectanomalies. HowDoCNNsWork? CNN'shavemultiplelayersthatprocessandextractfeaturesfromdata: ConvolutionLayer CNNhasaconvolutionlayerthathasseveralfilterstoperformtheconvolutionoperation. RectifiedLinearUnit(ReLU) CNN'shaveaReLUlayertoperformoperationsonelements.Theoutputisarectifiedfeaturemap. PoolingLayer Therectifiedfeaturemapnextfeedsintoapoolinglayer.Poolingisadown-samplingoperationthatreducesthedimensionsofthefeaturemap.  Thepoolinglayerthenconvertstheresultingtwo-dimensionalarraysfromthepooledfeaturemapintoasingle,long,continuous,linearvectorbyflatteningit.  FullyConnectedLayer Afullyconnectedlayerformswhentheflattenedmatrixfromthepoolinglayerisfedasaninput,whichclassifiesandidentifiestheimages. BelowisanexampleofanimageprocessedviaCNN. 2.LongShortTermMemoryNetworks(LSTMs) LSTMsareatypeofRecurrentNeuralNetwork(RNN)thatcanlearnandmemorizelong-termdependencies.Recallingpastinformationforlongperiodsisthedefaultbehavior.  LSTMsretaininformationovertime.Theyareusefulintime-seriespredictionbecausetheyrememberpreviousinputs.LSTMshaveachain-likestructurewherefourinteractinglayerscommunicateinauniqueway.Besidestime-seriespredictions,LSTMsaretypicallyusedforspeechrecognition,musiccomposition,andpharmaceuticaldevelopment. HowDoLSTMsWork? First,theyforgetirrelevantpartsofthepreviousstate  Next,theyselectivelyupdatethecell-statevalues Finally,theoutputofcertainpartsofthecellstate BelowisadiagramofhowLSTMsoperate: FreeCourse:IntroductiontoNeuralNetworkLearntheFundamentalsofNeuralNetworkEnrollNow 3.RecurrentNeuralNetworks(RNNs) RNNshaveconnectionsthatformdirectedcycles,whichallowtheoutputsfromtheLSTMtobefedasinputstothecurrentphase.  TheoutputfromtheLSTMbecomesaninputtothecurrentphaseandcanmemorizepreviousinputsduetoitsinternalmemory.RNNsarecommonlyusedforimagecaptioning,time-seriesanalysis,natural-languageprocessing,handwritingrecognition,andmachinetranslation. AnunfoldedRNNlookslikethis: HowDoRNNswork? Theoutputattimet-1feedsintotheinputattimet.  Similarly,theoutputattimetfeedsintotheinputattimet+1. RNNscanprocessinputsofanylength.  Thecomputationaccountsforhistoricalinformation,andthemodelsizedoesnotincreasewiththeinputsize. HereisanexampleofhowGoogle’sautocompletingfeatureworks: PostGraduatePrograminAIandMachineLearningInPartnershipwithPurdueUniversityExploreCourse 4.GenerativeAdversarialNetworks(GANs) GANsaregenerativedeeplearningalgorithmsthatcreatenewdatainstancesthatresemblethetrainingdata.GANhastwocomponents:agenerator,whichlearnstogeneratefakedata,andadiscriminator,whichlearnsfromthatfalseinformation. TheusageofGANshasincreasedoveraperiodoftime.Theycanbeusedtoimproveastronomicalimagesandsimulategravitationallensingfordark-matterresearch.VideogamedevelopersuseGANstoupscalelow-resolution,2Dtexturesinoldvideogamesbyrecreatingthemin4Korhigherresolutionsviaimagetraining. GANshelpgeneraterealisticimagesandcartooncharacters,createphotographsofhumanfaces,andrender3Dobjects. HowDoGANswork? Thediscriminatorlearnstodistinguishbetweenthegenerator’sfakedataandtherealsampledata. Duringtheinitialtraining,thegeneratorproducesfakedata,andthediscriminatorquicklylearnstotellthatit'sfalse. TheGANsendstheresultstothegeneratorandthediscriminatortoupdatethemodel. BelowisadiagramofhowGANsoperate: MasterdeeplearningconceptsandtheTensorFlowopen-sourceframeworkwiththe DeepLearningTrainingCourse.Getskilledtoday! 5.RadialBasisFunctionNetworks(RBFNs) RBFNsarespecialtypesoffeedforwardneuralnetworksthatuseradialbasisfunctionsasactivationfunctions.Theyhaveaninputlayer,ahiddenlayer,andanoutputlayerandaremostlyusedforclassification,regression,andtime-seriesprediction. HowDoRBFNsWork? RBFNsperformclassificationbymeasuringtheinput'ssimilaritytoexamplesfromthetrainingset. RBFNshaveaninputvectorthatfeedstotheinputlayer.TheyhavealayerofRBFneurons. Thefunctionfindstheweightedsumoftheinputs,andtheoutputlayerhasonenodepercategoryorclassofdata. TheneuronsinthehiddenlayercontaintheGaussiantransferfunctions,whichhaveoutputsthatareinverselyproportionaltothedistancefromtheneuron'scenter. Thenetwork'soutputisalinearcombinationoftheinput’sradial-basisfunctionsandtheneuron’sparameters. SeethisexampleofanRBFN: 6.MultilayerPerceptrons(MLPs) MLPsareanexcellentplacetostartlearningaboutdeeplearningtechnology.  MLPsbelongtotheclassoffeedforwardneuralnetworkswithmultiplelayersofperceptronsthathaveactivationfunctions.MLPsconsistofaninputlayerandanoutputlayerthatarefullyconnected.Theyhavethesamenumberofinputandoutputlayersbutmayhavemultiplehiddenlayersandcanbeusedtobuildspeech-recognition,image-recognition,andmachine-translationsoftware. HowDoMLPsWork? MLPsfeedthedatatotheinputlayerofthenetwork.Thelayersofneuronsconnectinagraphsothatthesignalpassesinonedirection. MLPscomputetheinputwiththeweightsthatexistbetweentheinputlayerandthehiddenlayers. MLPsuseactivationfunctionstodeterminewhichnodestofire.ActivationfunctionsincludeReLUs,sigmoidfunctions,andtanh. MLPstrainthemodeltounderstandthecorrelationandlearnthedependenciesbetweentheindependentandthetargetvariablesfromatrainingdataset. BelowisanexampleofanMLP.Thediagramcomputesweightsandbiasandappliessuitableactivationfunctionstoclassifyimagesofcatsanddogs. MachineLearningFreeCourseStartLearningToday'sMostIn-DemandSkillsExploreCourse 7.SelfOrganizingMaps(SOMs)  ProfessorTeuvoKohoneninventedSOMs,whichenabledatavisualizationtoreducethedimensionsofdatathroughself-organizingartificialneuralnetworks.  Datavisualizationattemptstosolvetheproblemthathumanscannoteasilyvisualizehigh-dimensionaldata.SOMsarecreatedtohelpusersunderstandthishigh-dimensionalinformation. HowDoSOMsWork? SOMsinitializeweightsforeachnodeandchooseavectoratrandomfromthetrainingdata. SOMsexamineeverynodetofindwhichweightsarethemostlikelyinputvector.ThewinningnodeiscalledtheBestMatchingUnit(BMU). SOMsdiscoverthe BMU’sneighborhood,andtheamountofneighborslessensovertime. SOMsawardawinningweighttothesamplevector.ThecloseranodeistoaBMU,themoreitsweightchanges.. ThefurthertheneighborisfromtheBMU,thelessitlearns.SOMsrepeatsteptwoforNiterations. Below,seeadiagramofaninputvectorofdifferentcolors.ThisdatafeedstoaSOM,whichthenconvertsthedatainto2DRGBvalues.Finally,itseparatesandcategorizesthedifferentcolors. 8.DeepBeliefNetworks(DBNs) DBNsaregenerativemodelsthatconsistofmultiplelayersofstochastic,latentvariables.Thelatentvariableshavebinaryvaluesandareoftencalledhiddenunits. DBNsareastackofBoltzmannMachineswithconnectionsbetweenthelayers,andeachRBMlayercommunicateswithboththepreviousandsubsequentlayers.DeepBeliefNetworks(DBNs)areusedforimage-recognition,video-recognition,andmotion-capturedata.  HowDoDBNsWork? GreedylearningalgorithmstrainDBNs.Thegreedylearningalgorithmusesalayer-by-layerapproachforlearningthetop-down,generativeweights. DBNsrunthestepsofGibbssamplingonthetoptwohiddenlayers.ThisstagedrawsasamplefromtheRBMdefinedbythetoptwohiddenlayers. DBNsdrawasamplefromthevisibleunitsusingasinglepassofancestralsamplingthroughtherestofthemodel. DBNslearnthatthevaluesofthelatentvariablesineverylayercanbeinferredbyasingle,bottom-uppass. BelowisanexampleofDBNarchitecture: BuilddeeplearningmodelsinTensorFlowandlearntheTensorFlowopen-sourceframeworkwiththeDeepLearningCourse(withKeras&TensorFlow).Enrollnow! 9.RestrictedBoltzmannMachines(RBMs) DevelopedbyGeoffreyHinton,RBMsarestochasticneuralnetworksthatcanlearnfromaprobabilitydistributionoverasetofinputs.  Thisdeeplearningalgorithmisusedfordimensionalityreduction,classification,regression,collaborativefiltering,featurelearning,andtopicmodeling.RBMsconstitutethebuildingblocksofDBNs. RBMsconsistoftwolayers: Visibleunits  Hiddenunits Eachvisibleunitisconnectedtoallhiddenunits.RBMshaveabiasunitthatisconnectedtoallthevisibleunitsandthehiddenunits,andtheyhavenooutputnodes. HowDoRBMsWork? RBMshavetwophases:forwardpassandbackwardpass. RBMsaccepttheinputsandtranslatethemintoasetofnumbersthatencodestheinputsintheforwardpass. RBMscombineeveryinputwithindividualweightandoneoverallbias.Thealgorithmpassestheoutputtothehiddenlayer. Inthebackwardpass,RBMstakethatsetofnumbersandtranslatethemtoformthereconstructedinputs. RBMscombineeachactivationwithindividualweightandoverallbiasandpasstheoutputtothevisiblelayerforreconstruction. Atthevisiblelayer,theRBMcomparesthereconstructionwiththeoriginalinputtoanalyzethequalityoftheresult. BelowisadiagramofhowRBMsfunction: 10.Autoencoders Autoencodersareaspecifictypeoffeedforwardneuralnetworkinwhichtheinputandoutputareidentical.GeoffreyHintondesignedautoencodersinthe1980stosolveunsupervisedlearningproblems.Theyaretrainedneuralnetworksthatreplicatethedatafromtheinputlayertotheoutputlayer.Autoencodersareusedforpurposessuchaspharmaceuticaldiscovery,popularityprediction,andimageprocessing. HowDoAutoencodersWork? Anautoencoderconsistsofthreemaincomponents:theencoder,thecode,andthedecoder. Autoencodersarestructuredtoreceiveaninputandtransformitintoadifferentrepresentation.Theythenattempttoreconstructtheoriginalinputasaccuratelyaspossible.  Whenanimageofadigitisnotclearlyvisible,itfeedstoanautoencoderneuralnetwork.  Autoencodersfirstencodetheimage,thenreducethesizeoftheinputintoasmallerrepresentation. Finally,theautoencoderdecodestheimagetogeneratethereconstructedimage. Thefollowingimagedemonstrateshowautoencodersoperate: Stayaheadofthetech-gamewithourPGPrograminAIandMachineLearninginpartnershipwithPurdueandincollaborationwithIBM.Exploremore! Conclusion Deeplearninghasevolvedoverthepastfiveyears,anddeeplearningalgorithmshavebecomewidelypopularinmanyindustries.Ifyouarelookingtogetintotheexcitingcareerofdatascienceandwanttolearnhowtoworkwithdeeplearningalgorithms,checkoutourAIandMLcoursestrainingtoday. DoexplorethefrequentlyaskedDeepLearninginterviewquestions,andunlockyourcareerasadatascientist! Ifyouhavedeeplearningalgorithmquestionsafterreadingthisarticle,pleaseleavetheminthecommentssection,andSimplilearn’steamofexpertswillreturnwithanswersshortly. FindourPostGraduatePrograminAIandMachineLearningOnlineBootcampintopcities:NameDatePlacePostGraduatePrograminAIandMachineLearningCohortstartson9thAug2022,WeekendbatchYourCityViewDetailsPostGraduatePrograminAIandMachineLearningCohortstartson17thAug2022,WeekendbatchSingaporeViewDetailsAbouttheAuthorAvijeetBiswalAvijeetisaSeniorResearchAnalystatSimplilearn.PassionateaboutDataAnalytics,MachineLearning,andDeepLearning,Avijeetisalsointerestedinpolitics,cricket,andfootball.ViewMoreRecommendedResourcesDiscovertheDifferencesBetweenAIvs.MachineLearningvs.DeepLearningVideoTutorialMachineLearningInterviewGuideEbookCourseReview:TrainingforaCareerinAIandMachineLearningArticleProgramPreview:ALiveLookattheCaltechCTMEPostGraduatePrograminDevOpsWebinarHowtoBecomeaMachineLearningEngineer?VideoTutorialMachineLearningCareerGuide:AcompleteplaybooktobecomingaMachineLearningEngineerEbookprevNext DisclaimerPMP,PMI,PMBOK,CAPM,PgMP,PfMP,ACP,PBA,RMP,SP,andOPM3areregisteredmarksoftheProjectManagementInstitute,Inc.