deep learning techniques and applications深度學習技術(shù)與應用

DeepDeepLearning:Techniques DeepChallengetoThetruechallengetoartificialinprovedtobesolvingthetasksthatareeasyforpeopletoperformbuthardforpeopletodescribeformally;solvingtheproblemsthatwesolveintuitively,thatfeelautomatic;likerecognizingspokenwordsorfacesinFlowchartsshowinghowthedifferentpartsofanAIsystemrelatetoeachotherwithindifferentAIdisciplines.Shadedboxesindicatecomponentsthatareabletolearnfromdata.Thefigureshowstwoofthethreehistoricalwavesofartificialneuralnetsresearch,asmeasuredbythefrequencyoftheand“connectionism”or“neuralnetworks”accordingtoDeeplearninghasemoreusefulastheamountofavailabletrainingdatahasKeyTrendsaboutDeepDeeplearningmodelshavegrowninsizeovertimeascomputerhardwareandsoftwareinfrastructurefordeeplearninghasKeyTrendsaboutDeepDeeplearningmodelshavegrowninsizeovertimeascomputerhardwareandsoftwareinfrastructurefordeeplearninghasKeyTrendsaboutDeepDeeplearninghassolvedincreasinglywithincreasingaccuracyovertime.2009

微 的語音識別專家LiDeng和DongYu和Hinton開始合作2011微軟的語音識別研究取得成把連續(xù)多幀的語音特征并在一起，構(gòu) 特征逐級地進行信息特征抽取無縫地和傳統(tǒng)的語音識別技術(shù)相結(jié)合，在不引起任何系統(tǒng)額外耗況下大幅度地提升語音識別系統(tǒng)的識別率，LeCun,ConvolutionNeuralNetworks,2012年10月 團隊 分類問題上用更深 N取最好結(jié)果，使得圖像識 2015 公布 錯誤率DeepDeepLearninginNLP&WhyNeuralThehumancerebralcortexis2to4millimetresinThedifferentcorticallayerseachcontainacharacteristicdistributionneuronalcelltypesandconnectionswithothercorticalandsubcorticalthemoreancientpartofthecerebralcortex,thehippocampus,hasatthreecellularThemostrecentpartofthecerebralcortex,theneocortex(alsocalledisocortex),NeuronsinvariouslayersconnectverticallytoformsmallThecortexisorganizedverticallyincolumnsandhorizontallyinThedifferentregionsofsomatosensoryreceivetheirmaininputsfromdifferentkindsofreceptors.Area3breceivesmostofitsprojectionsfromthesuperficialArea3areceivesinputfromreceptorsinthemuscleInputfromthethalamusarrivesatlayerIV,whereneuronsdistributeinformationupanddownlayers.[Kaasetal.,1979.]Hubel-WieselHubel,DavidH.,andTorstenN.Wiesel."Receptivefieldsofsingleneuronesinthecat'sstriatecortex."TheJournalofphysiology148,no.3(1959):574-591.Hubel-WieselTheNobelPrizeinPhysiologyorMedicine,FurtherBrunoDavidCornell 上不同物UniversityofToronto-MachineLearningGroup(GeoffHinton,RichZemel,RuslanSalakhutdinov,BrendanFrey,RadfordUniversitédeMontréal-LisaLab(YoshuaBengio,PascalVincent,AaronCourville,RolandNewYorkUniversity–YannLecun‘sandRobFergus‘StanfordUniversity–AndrewNg‘sUniversityofOxford–Deeplearninggroup,NandodeFreitasandPhilResearch–JeffDean,SamyBengio,JasonWeston,Marc’AurelioRanzato,DumitruErhan,QuocLeetResearch–LiDengetSUPSI–IDSIA(Jurgen UCBerkeley–BrunoOlshausen‘sUniversityofWashington–PedroDomingos‘IDIAPResearchInstitute-RonanCollobert‘sUniversityofCaliforniaMerced–MiguelA.Carreira-Perpinan‘sUniversityofHelsinki-AapoHyv?rinen‘sNeuroinformaticsUniversitédeSherbrooke–Hugo e‘sUniversityofGuelph–GrahamTaylor‘sUniversityofMichigan–HonglakLee‘sTechnicalUniversityofBerlin–Klaus-RobertMuller‘sBaidu–KaiYu‘sAaltoUniversity-JuhaKarhunenandTapaniRaikoU.Amsterdam–MaxWelling‘sU.CaliforniaIrvine–PierreBaldi‘sGhentUniversity–BenjaminShrauwen‘sUniversityofTennessee–ItamarArel‘sIBMResearch–BrianKingsburyetUniversityofBonn–SvenBehnke’sGatsbyUnit@UniversityCollegeLondon–ManeeshSahani,Yee-WhyeTeh,PeterComputationalCognitiveNeuroscienceLab@UniversityofColoradoThere'saninterestinghistoryaboutpeople'schangesintheirattitudestowarddeeparchitecturesandtheshallow2012-Asurveyondeeplearning-onesmallsteptowardHistoricalContextofDeepInaround1960,the1stgenerationofneuralnetworkwasborn(byIt'scapabilityofclassifyingsomebasicshapesliketrianglesandsquaresletpeopleseethepotentialthatarealin ligentmachinewhichcansense,learn,rememberandrecognizelikehuman-beingscanbeinventedwiththistrend.BUT,itsfundamentallimitationssoonbrokepeople'sCriticizingfromMarvinMinsky,OneoftheapparentreasonsisthatthefeaturelayerofthisPerceptronisfixedandcraftedbyhumanbeings,whichisabsolu againstthedefinitionofareal“in Anotherreasonisitssingle-layerstructurelimitsthefunctionsitlearn,e.g,anexclusive-orfunctionisoutofitslearningInaround1985,basedonthePerceptrons,Geoffrey cedtheoriginalsinglefixedfeaturelayerwithseveralhiddenlayers,creatingthe2nd-generationneuralnetwork.viaBack-propagationalgorithm(proposedin1969,practicableinBPdidnotworkwellinThecorrectingsignalwillbeweakenedwhenitpassesbackviamultipleItoftengetstrappedinpoorlocaloptimawhenthebatch-modeorevengradientdescentBPalgorithmisTheseverityincreasessignificantlyasthedepthofthenetworksLearningistooslowacrossmultiplehiddenIn1989,YannLeCunetal.builtadeepneuralnetworkwiththepurposerecognizinghandwrittenZIPcodesonDespitethesuccessofapplyingthealgorithm,thetimetotrainthenetworkondatasetwas y3SVMsloweddownthedevelopmentsofWhenpeopleweretryingtomakeimprovementstoHinton'sneuralnetworkswithrespecttothoseadvantagesTryingtoincreasethetrainingdatasetandestimatingtheinitialweight1993-1995VladimirN.Vapnik,etmadeimprovementsontheoriginalTheycreatinganewfamilySupportVectorSVM——GoodorSVMmakeslearningfastandeasy,duetoitssimpleAppropriatefordatawithsimplestructures,e.g,withasmalloffeaturesorthedatawhichdoesn'tcontainhierarchicalBut,forthedatawhichitselfcontainscomplicatedfeatures,tendstoperformworsebecauseofitssimpleOnewaytosolvethisproblemistoaddapriorknowledgetotheSVMmodelinordertoobtainabetterfeaturelayer.But,it'shardtofindageneralsetofpriorSVM——takesusawayfromtheroadtoareal ligentmachineDespitethefactthatSVMcanworkreallywellinsolvingmanyAIproblems,itisnotagoodtrendtoAIduetoitsfataldeficiency,shallowarchitecture.SVMisstillakindofPerceptronwherethefeaturesareobtainednotlearntfromthedataWiththepurposeoffindinganarchitecturethatmeetstherequirementsabove,someresearchersstartedtolookbacktothemulti-layerneuralnetwork,tryingtoexploititsadvantagesrelatedtodeepandethelimitations...After2010年 國防部DARPA計劃首次資助深度學習項目，參與方包括福大學、紐約大學和 2011年，微 和谷歌的語音識別研究人員先后采用DNN技術(shù)降低語識別錯誤率20%-30%，是該領(lǐng)域10年來最大突2012年，Hinton將 分類問題的Top5錯誤率由26%降至至15%Andrewf建 個音和；2013年，Hinton創(chuàng)立的DNNResearch公司 收購2013年，YannLeCun加 的人工智 After CTR預估（Click-Through-RatePrediction， 檢索達到了國際領(lǐng)先水平。2014年，AndrewNg加盟。2013年，騰訊著手建立深度學臺Mariana。Mariana面向語音識別、圖像識別、推薦等眾多應用領(lǐng)域，提供默認算法的并行實現(xiàn)以減少新算DNNGPUDNNCPUBMBM->RBM-BM->RBMBM->RBM-DBN模型被視為由若干個RBM堆疊在一起，可過由低到高逐層訓練這些RBM來實現(xiàn)（1）底部RBM以原始輸入數(shù)據(jù)訓（2）將底部RBM抽取的特征作為頂部的輸入訓Hintonin自 ，80年代晚期出主要用于降維，后用于主成分分??輸入輸出層神經(jīng)元個m隱藏層神經(jīng)元個p，q各層神經(jīng)元的偏w輸入層與隱藏層之間的權(quán)?隱藏層與輸出層之間的權(quán)Y.Bengio,P.Lamblin,D.Popovici,andH. e.Greedylayerwisetrainingofnetworks.InProceedingsofNeuralInformationProcessingSystems(NIPS).2012Hinton–Deep2014/12/9NetworkIn2013ZFVisualizingandUnderstandingConvolutional2014/12/9NetworkIn RNN(RecurrentNeuralallbiologicalneuralnetworksareRNNsimplementdynamicalmathematically,SomeOld