Deep Learning 101: Introduction [Pros, Cons & Uses] - V7 Labs

The deep learning model maps the input and the output to find a correlation between them. This correlation can be then used to cluster, predict, ... Platformv7platformImageAnnotationLabeldatadelightfullyDatasetManagementAllyourtrainingdatainoneplaceModelTrainingAutomateyourdataworkflowsfeaturesAutomatedAnnotationThemostaccurateautomatedannotationLabelingServicesOutsourceyourannotationtotheprosVideoAnnotationAnnotatevideoswithoutframerateerrorsDocumentProcessingAutomateOCRandIDPworkflowsIndustriesindustriesAgricultureAutomotiveConstructionEnergyFood& BeverageGovernmentHealthcareInsurance& FinanceLifeSciences& BiotechLogisticsManufacturingFood & DrinkRetailSoftware&InternetSportsAllIndustries->customerstoriesMonitoringthehealthofcattlethroughcomputervisionHowCattleEyeUsesV7toDevelopAIModels10xFasterDevelopingAI-poweredultrasoundsimulationtechnologiesHowIntelligentUltrasoundusedV7toDoubletheSpeedoftheirTrainingDataPipelinesAllCustomerStories->CompanyAboutUsContactUsNewsDataSecurityJobsWe’rehiring!fromthev7blogKnowledgeDistillation:Principles&Algorithms[+Applications]TheEssentialGuidetoPytorchLossFunctionsDeepLearningforImageSuper-Resolution[incl.Architectures]Readfromourblog->PricingCommunityBlogDocsAcademyOpenDatasetsnewsV7ReleasesDeepFakeDetectorforChromeV7atCVPR2022V7SupportsMoreFormatsforMedicalImageAnnotationAnnotatedDatasettoAidCOVID-19ResearchAvailableonGitHubLoginRequestaDemov7platformImageAnnotationDatasetManagementModelTrainingcompanyAboutUsContactUsJobsDataSecurityBlogPricingNewsFeaturesAutomatedAnnotationLabeling ServicesVideoAnnotationDocumentProcessingindustriesHealthcareRetailAgricultureAllIndustries->customerstoriesHowIntelligentUltrasoundusedV7toDoubletheSpeedoftheirTrainingDataPipelinesDevelopingAI-poweredultrasoundsimulationtechnologiesHowCattleEyeUsesV7toDevelopAIModels10xFasterMonitoringthehealthofcattlethroughcomputervisionAllCustomerStories->BLOGDeepLearningAGentleIntroductiontoDeepLearning—theELI5WayWhatisDeepLearning,howdoesitwork,andwhatareitsmostcommonapplications?Here'sthemostcomprehensiveguidetoDeepLearningforbeginners.13minread  ·  July19,2022NileshBarlaPerceptronAIContentsHere'saninterestingfact—Eachmonth,thereare186.000Googlesearchesforthekeyword"deeplearning."It'saboilinghotareaofresearch,andthewordisout—DeepLearningisapromisingtechnologythatcanradicallytransformtheworldwelivein.Nowonderit'sbeengainingtractionandattractingtheattentionofresearchers,AI-firstbusinesses,andmediaalike.Thechancesarethatyou'velandedonthispagelookingforanexplanationofwhatDeepLearningisallaboutandwhyyoushouldcare.Thegoodnewsis—we'vegottheanswersyouarelookingfor.AndwearehappytoexplaintheminplainEnglish.Here’swhatwe’llcover:WhatisDeepLearning?DeepLearningvs.MachineLearningHowdoesDeepLearningwork?HowtocreateandtrainDeepLearningmodelsDeepLearninglimitations4DeepLearningapplicationsBestDeepLearningresourcesAndifyouwanttoskipthewrittenguide,makesuretocheckoutthisdetailedvideointroductiontoDeepLearning.Now,let'sbreakthingsdown!Solveanyvideoorimagelabelingtask10xfasterandwith10xlessmanualwork. 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WhatisDeepLearning?DeepLearningisasubsetofMachineLearningthatusesmathematicalfunctionstomaptheinputtotheoutput.Thesefunctionscanextractnon-redundantinformationorpatternsfromthedata,whichenablesthemtoformarelationshipbetweentheinputandtheoutput.Thisisknownaslearning,andtheprocessoflearningiscalledtraining.💡Protip:CheckoutV7ModelTrainingtolearnmore. Intraditionalcomputerprogramming,inputandasetofrulesarecombinedtogethertogetthedesiredoutput.Inmachinelearninganddeeplearning,inputandoutputarecorrelatedtotherules.Theserules—whencombinedwithnewinput—yielddesiredresults.Source:DeepLearningwithPythonbyFrançoisCholletModerndeeplearningmodelsuseartificialneuralnetworksorsimplyneuralnetworkstoextractinformation.Theseneuralnetworksaremadeupofasimplemathematicalfunctionthatcanbestackedontopofeachotherandarrangedintheformoflayers,givingthemasenseofdepth,hencethetermDeepLearning.DeeplearningcanalsobethoughtofasanapproachtoArtificialIntelligence,asmartcombinationofhardwareandsoftwaretosolvetasksrequiringhumanintelligence.ArtificialIntelligencevsMachineLearningvsDeepLearningDeepLearningwasfirsttheorizedinthe1980s,butithasonlybecomeusefulrecentlybecause:ItrequireslargeamountsoflabeleddataItrequiressignificantcomputationalpower(highperformingGPUs)IfyouarecurioustolearnmoreabouttheuseofAIacrossvariousindustries,checkout:6ViableAIUseCasesinInsurance7Game-ChangingAIApplicationsintheSportsIndustry6AIApplicationsShapingtheFutureofRetail7Out-of-the-BoxApplicationsofAIinManufacturing8PracticalApplicationsofAIInAgriculture7Life-SavingAIUseCasesinHealthcareNext,we'lldefinethekeyelementsthatmakeuptheDeepLearningalgorithms.💡Protip:Ifyouarelookingforafreeimageannotationtool,checkoutTheCompleteGuidetoCVAT—Pros&Cons[2022].NeuralNetworksTheneuralnetworkistheheartofdeeplearningmodels,anditwasinitiallydesignedtomimictheworkingoftheneuronsinthehumanbrain.Hereareitscomponents.NeuroninArtificialNeuralNetworkcomponentsTheneuronalperceptionofdeeplearningisgenerallymotivatedbytwomainideas:Itisassumedthatthehumanbrainprovesthatintelligentbehaviorispossible,and—byreverseengineering,itispossibletobuildanintelligentsystemAnotherperspectiveisthattounderstandtheworkingofthehumanbrainandtheprinciplesthatunderlieitsintelligenceistobuildamathematicalmodelthatcouldshedlightonthefundamentalscientificquestions.Inessence,neuralnetworksenableustolearnthestructureofthedataorinformationandhelpustounderstanditbyperformingtaskssuchasclustering,classification,regression,orsamplegeneration.DeepLearningvs.MachineLearningWhyisDeepLearningmorepowerfulthantraditionalMachineLearning?DeepLearningcanessentiallydoeverythingthatmachinelearningdoes,butnottheotherwayaround.Forinstance,machinelearningisusefulwhenthedatasetissmallandwell-curated,whichmeansthatthedataiscarefullypreprocessed.Datapreprocessingrequireshumanintervention.Italsomeansthatwhenthedatasetislargeandcomplex,machinelearningalgorithmswillfailtoextractinformation,anditwillunderfit.💡Protip:Lookingforqualitytrainingdata?Checkout65+BestFreeDatasetsforMachineLearning.Generally,machinelearningisalternativelytermedshallowlearningbecauseitisveryeffectiveforsmallerdatasets.Deeplearning,ontheotherhand,isextremelypowerfulwhenthedatasetislarge.Itcanlearnanycomplexpatternsfromthedataandcandrawaccurateconclusionsonitsown.Infact,deeplearningissopowerfulthatitcanevenprocessunstructureddata—datathatisnotadequatelyarrangedliketextcorpus,socialmediaactivity,etc.Furthermore,itcanalsogeneratenewdatasamplesandfindanomaliesthatmachinelearningalgorithmsandhumaneyescanmiss.Theperformanceofdeeplearningvs.otherlearningalgorithmsOnthedownside,deeplearningiscomputationallyexpensivecomparedtomachinelearning,whichalsomeansthatitrequiresalotoftimetoprocess.DeepLearningandMachineLearningarebothcapableofdifferenttypesoflearning:SupervisedLearning(labeleddata),UnsupervisedLearning(unlabeleddata),andReinforcementLearning.Buttheirusefulnessisusuallydeterminedbythesizeandcomplexityofthedata.💡Protip:LearnmoreaboutSupervisedvs.UnsupervisedLearning.Tosummarize:Machinelearningrequiresdatapreprocessing,whichinvolveshumanintervention.Theneuralnetworksindeeplearningarecapableofextractingfeatures;hencenohumaninterventionisrequired.DeepLearningcanprocessunstructureddata.DeepLearningisusuallybasedonrepresentativelearningi.e.,findingandextractingvitalinformationorpatternsthatrepresenttheentiredataset. Deeplearningiscomputationallyexpensiveandtime-consuming.💡Protip:Checkout20+OpenSourceComputerVisionDatasetstofindqualitydata.HowdoesDeepLearningwork?Now,let'sdiveintolearnhowDeepLearningworks.DeepNeuralNetworkshavemultiplelayersofinterconnectedartificialneuronsornodesthatarestackedtogether.Eachofthesenodeshasasimplemathematicalfunction—usuallyalinearfunctionthatperformsextractionandmappingofinformation.Therearethreelayerstoadeepneuralnetwork:theinputlayer,hiddenlayers,andtheoutputlayer.DeepneuralnetworkarchitectureThedataisfedintotheinputlayer.Eachnodeintheinputlayeringeststhedataandpassesitontothenextlayer,i.e.,thehiddenlayers.Thesehiddenlayersincreasinglyextractfeaturesfromthegiveninputlayerandtransformitusingthelinearfunction.Theselayersarecalledhiddenlayersbecausetheparameters(weightsandbiases)ineachnodeareunknown;theselayersaddrandomparameterstotransformthedata,eachofwhichyieldsdifferentoutput.💡Protip:Read12TypesofNeuralNetworkActivationFunctions:HowtoChoose?Theoutputyieldedfromthehiddenlayersisthenpassedontothefinallayercalledtheoutputlayer,wheredependinguponthetask,itclassifies,predicts,orgeneratessamples.Thisprocessiscalledforwardpropagation.ForwardpropagationinneuralnetworksInanotherprocesscalledbackpropagation,analgorithm,likegradientdescent,calculateserrorsbytakingthedifferencebetweenthepredictedoutputandtheoriginaloutput.Thiserroristhenadjustedbyfine-tuningtheweightsandbiasesofthefunctionbymovingbackwardthroughthelayers.Both,theprocessofforwardpropagationandbackpropagationallowsaneuralnetworktoreducetheerrorandachievehighaccuracyinaparticulartask.Witheachiteration,thealgorithmbecomesgraduallymoreaccurate.TypesofneuralnetworksThereareseveraltypesofneuralnetworks.CNNTheConvolutionalNeuralNetworksorCNNsareprimarilyusedfortasksrelatedtocomputervisionorimageprocessing.CNNsareextremelygoodinmodelingspatialdatasuchas2Dor3Dimagesandvideos.Theycanextractfeaturesandpatternswithinanimage,enablingtaskssuchasimageclassificationorobjectdetection.ConvolutionalNeuralNetworksRNNTheRecurrentNeuralNetworksorRNNareprimarilyusedtomodelsequentialdata,suchastext,audio,oranytypeofdatathatrepresentssequenceortime.Theyareoftenusedintasksrelatedtonaturallanguageprocessing(NLP).TheRecurrentNeuralNetworksarchitectureGANGenerativeadversarialnetworksorGANsareframeworksthatareusedforthetasksrelatedtounsupervisedlearning.Thistypeofnetworkessentiallylearnsthestructureofthedata,andpatternsinawaythatitcanbeusedtogeneratenewexamples,similartothatoftheoriginaldataset.GenerativeadversarialnetworksTransformersTransformersarethenewclassdeeplearningmodelthatisusedmostlyforthetasksrelatedtomodelingsequentialdata,likethatinNLP.ItismuchmorepowerfulthanRNNsandtheyarereplacingthemineverytask.Recently,transformersarealsobeingappliedincomputervisiontasksandtheyareprovingtobequiteeffectivethanthetraditionalCNNs.TransformerNeuralNetworksHowtoCreateandTrainDeepLearningModelsInthissection,we'lldiscusstwodistinctstrategiesfortrainingdeeplearningmodels.TrainfromscratchTotrainadeepnetworkfromscratch,weneedtohaveaccesstoalargedataset,whichyoucanfindonline.Onceyouhavecollectedthedata,youneedtodesignadeepneuralnetworkthatwillextractandlearnthefeaturesofthedataset.Designingadeepneuralnetworkcanbeatedioustask.Inordertogetstarted,youcanmakeuseoftheV7.Here'saquicktutorial:1.Signupforthe14-dayfreetrialV7nowoffersyouthreemodelsthatyoucanexploreandtrain:ImageClassification,ObjectDetection,InstanceSegmentation.V7modeltrainingV7alsocomeswithapublic,in-builtTextScanner(OCR)modelthatyoucanusefordocumentprocessing.💡Protip:LearnmoreaboutOpticalCharacterRecognition.2.Togetstarted,gotothemaindashboardofV7andclickonthe‘NeuralNetworks’tabontheleft.V7NeuralNetworkModelsdashboard3.Onceyouarein,youcanthenclickonthe+NEWMODELbuttononthetopright-handcorner,thiswillnavigateyoutothemenupage,whereyouwillfindthethreemodels:InstanceSegmentationObjectDetectionClassificationLetusbrieflywalkyouthroughthetrainingoftheinstancesegmentationmodel.4.SelecttheModelcardandclick‘Continue’whichwilltakeyoutothenextpagetoselectyourdatasetfortraining.Pickingthedatasetfortraining5.Onceyouhaveselectedthedataset,clickon"Continue".Next,youwillseethebreakdownofthenumberofimagesthatwillbeusedfortraining,validation,andtesting.Training,validation,andtestsplit6.Clickon‘StartTraining’whichyouwillfindatthebottomrightofthedashboard.7.Oncethetrainingiscompleted,V7willnotifyyouviaemailthatyourmodelhasfinishedtrainingandisreadytouse.TransferLearningTransferlearningisanapproachwhereyouuseanexistingpre-trainedmodelandfine-tuneitwithyourdesireddataset.Thisisthemostcommonapproach.NetworkssuchasAlexNetorGoogLeNet,VGG16,andVGG19aresomeofthemostcommonpre-trainednetworks.Transferlearninghasadvantagesovertrainingamodelfromscratchbecause:a)Youdon’tneedtodesignanentirearchitecturefromscratch.b)Thetrainingtimeisshorter.c)Youcantrainwithlessdata.💡Protip:Checkout15+TopComputerVisionProjectIdeasforBeginnersFor2022tostartbuildingyourownmodels.DeepLearningLimitationsWehopethatthisdoesnotcomeasasurprise,butit'sworthmentioningthatDeepLearning,indeed,hasseverallimitations.We'velistedafewofthembelow.DataavailabilityDeeplearningmodelsrequirealotofdatatolearntherepresentation,structure,distribution,andpatternofthedata.Ifthereisn'tenoughvarieddataavailable,thenthemodelwillnotlearnwellandwilllackgeneralization(itwon'tperformwellonunseendata).Themodelcanonlygeneralizewellifitistrainedonlargeamountsofdata.ThecomplexityofthemodelDesigningadeeplearningmodelisoftenatrialanderrorprocess.Asimplemodelismostlikelytounderfit,i.e.notabletoextractinformationfromthetrainingset,andaverycomplexmodelismostlikelytooverfit,i.e.,notabletogeneralizewellonthetestdataset.Deeplearningmodelswillperformwellwhentheircomplexityisappropriatetothecomplexityofthedata.LacksglobalgeneralizationAsimpleneuralnetworkcanhavethousandstotensofthousandsofparameters.Theideaofglobalgeneralizationisthatalltheparametersinthemodelshouldcohesivelyupdatethemselvestoreducethegeneralizationerrorortesterrorasmuchaspossible.However,becauseofthecomplexityofthemodel,itisverydifficulttoachievezerogeneralizationerroronthetestset.Hence,thedeeplearningmodelwillalwayslackglobalgeneralizationwhichcanattimesyieldwrongresults.IncapableofMultitaskingDeepneuralnetworksareincapableofmultitasking.Thesemodelscanonlyperformtargetedtasks,i.e.,processdataonwhichtheyaretrained.Forinstance,amodeltrainedonclassifyingcatsanddogswillnotclassifymenandwomen.Furthermore,applicationsthatrequirereasoningorgeneralintelligencearecompletelybeyondwhatthecurrentgeneration’sdeeplearningtechniquescando,evenwithlargesetsofdata.HardwaredependenceAsmentionedbefore,deeplearningmodelsarecomputationallyexpensive.ThesemodelsaresocomplexthatanormalCPUwillnotbeabletowithstandthecomputationalcomplexity.However,multicorehigh-performinggraphicsprocessingunits(GPUs)andtensorprocessingunits(TPUs)arerequiredtoeffectivelytrainthesemodelsinashortertime.Althoughtheseprocessorssavetime,theyareexpensiveanduselargeamountsofenergy.4DeepLearningApplicationsNow,let'shaveacloserlookatthemostimportantDeepLearningapplications.ApplicationsofdeeplearningDeepLearningfindsapplicationsin:Speechrecognition:SomeofthefamiliarsoftwarelikeApple’sSiri,Google’sAlexa,andMicrosoftCortanaarebasedondeepneuralnetworks.Patternrecognition:Patternrecognitionisveryusefulinmedicalandlifesciences.ThesealgorithmscanhelpradiologiststofindtumorcellsintheCTscansorevenhelpthemtounderstandthemechanismbehindproteinfolding.Furthermore,otherareassuchasfinancecanalsousepatternrecognitionsystemstodetectfraudulenttransactions.NLP:NaturallanguageprocessingorNLPisoneofthehottopicsindeeplearningthesedays.Modernarchitectureslikethetransformershaverevolutionizedandimprovedmachinetranslationandlanguagemodeling.OneofsuchmodelsisGPT3byOpe;ithasalmostreachedgeneralintelligenceinallNLPtasks.Recommendersystems:RecommendersystemsareonalmosteverysocialmediaplatformthesedaysfromInstagramtoYouTubeandNetflix.Thesecompaniesusearecommendationsystemtorecommendshows,videos,posts,andstoriesbasedonusers'activities.Real-lifeDeepLearningusecasesFinally,herearesomeofthereal-lifeusecasesofdeeplearning.HealthcareMedicalimageanalysis:MedicalimagessuchasCTscans,MRI,andX-rayscansometimesbedifficulttointerpret;thismostlyhappenswhentheanomalieslikethetumorgetsaturatedinthebackground.Deeplearningalgorithmscanhelptofindanomaliesthatareunseentothenakedeye.AlgorithmsliketheHierarchicalProbabilisticU-NetbyGoogle’sDeepMindisonesuchexamplethatiscapableoffindingtumorcellsinmedicalimages.Suchalgorithmsarefoundtobeagreattoolforradiologistsanddoctors.Surgicalrobotics:Therearetimeswhenacriticalpatientisunabletofindasurgeon;insuchdireandlife-threateningconditionssurgicalrobotscancometotherescue.Suchrobotshaveasuperhumanabilitytorepeatexactmotionslikethatofatrainedsurgeon.💡Protip:GotoMedicalImageAnnotationwithV7tolearnmore.TransportationSelf-drivingcars:Self-drivingcarsarebecomingoneofthetrendingtopicsintheworldrightnow.CompaniesTesla,Waymo,andothersarepushingthistrendofdevelopingtechnologyforsafedriving.Allthesecompaniesusedeeplearningastheircorealgorithm;thesemodelscanconsumealotofdataandenablethesecarstonavigatethroughroadswhilemakingcorrectdecisionsthroughanalyzingtheroadsandvehiclesaroundthem.Thesecarsaresoadvancedthattheycanevenpredictaccidents.Smartcities:Smartcitiescanmanagetheirresourcesefficientlyandmanagetraffic,publicservices,anddisasterresponse.Thewayitworksisthattheinputfromdifferentsensorsfromalloverthecitycanbeusedtocollectdataandadeeplearningsystemtrainedonthatdatacanbeusedtopredictdifferentoutputsbaseduponthescenario.AgricultureRobotpicking:Deeplearningcanbeusedtoenablerobotsthatcanclassifyandpickcrops.Theserobotscansavetimeandincreasetheproductionrateaswell.Cropandsoilmonitoring:Deeplearningmodeltrainedonthecropandsoilconditiondatacanbeusedtobuildasystemthatcaneffectivelymonitorcropandsoilhelpestimateyield.Livestockmonitoring:Animalscanmovefromoneplacetoanother,makingthemdifficulttomonitor.That’swhereimageannotationforcomputervisioncomesin.Imageannotationwithdeeplearningcanenablefarmerstotrackthelocation,predictthelivestock'sfoodneeds,andmonitortherestcycletoensurethattheyareingoodhealth.Plantdiseaseandpestdetection:Anotherusefulareafordeeplearninginagricultureistoclassifyplantssufferingfromthediseasefromhealthyplants.Thistypeofsystemcanhelpfarmerstakepropertreatmentoftheplantbeforetheydie.Furthermore,deeplearningcanalsobeusedtodetectpestinfestation.BestDeepLearningResourcesHungryformore?;-)CheckoutourTOP3DeepLearningresourcestolearnmore:https://www.youtube.com/watch?v=0VH1Lim8gL8https://www.deeplearningbook.orgNeuralNetworkProgrammingCourseDeepLearning:KeyTakeawaysWe'velearnedtodaythatDeeplearningisaveryversatiletool.Inspiredbythebiologicalbraindeeplearninghasprovenitsusefulnessinalmostallareasofscienceandengineering.Here'saquickrecapofeverythingwe'vediscussed:Adeeplearningmodelismadeofupaninterconnectedmultilayerneuralnetwork.Thebasicpartoftheneuralnetworkiscalledanode,whichissimplyamathematicallinearfunction.Thedeeplearningmodelmapstheinputandtheoutputtofindacorrelationbetweenthem.Thiscorrelationcanbethenusedtocluster,predict,classify,andevengeneratenewsamplesofdata.Oneneedstotrainadeeplearningmodeltomakeitlearnandproduceaccurateresults.Thetrainingprocessconsistsoftwosub-processescalledforwardpropagationandbackwardpropagation.Theformerbuildscorrelationbyassigningparameterswhilethelatteradjustthoseparameterswithrespecttotheerroritproduces. 💡Readnext:AStep-by-StepGuidetoTextAnnotation[+FreeOCRTool]TheCompleteGuidetoCVAT—Pros&Cons[2022]5AlternativestoScaleAITheUltimateGuidetoSemi-SupervisedLearning9EssentialFeaturesforaBoundingBoxAnnotationToolTheCompleteGuidetoEnsembleLearningTheBeginner’sGuidetoContrastiveLearning9ReinforcementLearningReal-LifeApplicationsMeanAveragePrecision(mAP)Explained:EverythingYouNeedtoKnowFREEApplyforanEducationPlanThankyou!Yoursubmissionhasbeenreceived!Oops!Somethingwentwrongwhilesubmittingtheform.RelatedarticlesDeepLearningTheBeginner'sGuidetoDeepReinforcementLearning[2022]PragatiBaheti10minreadDeepLearningTheEssentialGuidetoNeuralNetworkArchitecturesPragatiBaheti16minreadDeepLearningAGentleIntroductiontoDeepLearning—theELI5WayNileshBarla13minreadSubscribetoourblog1personalizedemailfromV7'sCEOpermonth->Thankyouforsubscribing!Oops!Somethingwentwrongwhilesubmittingtheform.NewsletterJoinover7,000+MLscientistslearningthesecretsofbuildinggreatAI.Thankyou!Yoursubmissionhasbeenreceived!Oops!Somethingwentwrongwhilesubmittingtheform.NotusingV7yet?