大家好，关于recall 人工智能很多朋友都还不太明白，不过没关系，因为今天小编就来为大家分享关于ir人工智能的知识点，相信应该可以解决大家的一些困惑和问题，如果碰巧可以解决您的问题，还望关注下本站哦，希望对各位有所帮助！

本文目录

1. 关于人工智能利弊的英文作文
2. 怎样看待人工智能英语作文
3. 人工智能一些术语总结
4. 浅谈人工智能产品设计——情感分析
5. 计算神经科学能否成为未来人工智能的发展方向？

关于人工智能利弊的英文作文

銆€銆€浜哄伐鏅鸿兘鐨勫嚭鐜帮紝浜哄伐鏅鸿兘鎴愪负鍐欎綔鐨勭礌鏉愶紝閭ｄ箞鏈夊摢浜涘叧浜庝汉宸ユ櫤鑳界殑浣滄枃鍛?涓嬮潰鏄垜涓轰綘鏁寸悊鐨勫叧浜庝汉宸ユ櫤鑳藉埄寮婄殑鑻辨枃浣滄枃锛屼緵澶у闃呰!

銆€銆€浜哄伐鏅鸿兘鍒╁紛鐨勮嫳鏂囦綔鏂囩涓€绡?/strong>

銆€銆€Artificialintelligence(ai)approach,someoneworriesabout

銆€銆€unemployment,somepeopleinthefuture,someoneinexploringbusinessopportunities,alsosomepeopleonthego.Beforediscussingthese,maybeweshouldconsidertheoutcomeofhumanbeings.

銆€銆€Onemightthinkaboutthistopictooexaggeration,

銆€銆€Thefirstrecallwhathashappenedinthehistoryofmankindincrediblethings.

銆€銆€Incrediblethings,theneedtopleaseafewthroughtodecide.Weplease1wasborninthe0peoplebornintheyearofthe(handynasty)through1600A.D.(Mingdynasty),althoughspans1600years,butthemanmaybeonthelivesofpeoplearoundyouwon'tfeeltooexaggerated,justchangedafewdynasty,stillfacingtheloessbackandbusyday.Butifplease11600Britishpeoplethroughto1850intheUK,seethehugesteelmonstersonthewaterran,thispersonmaydirectlybefrightenurine,thisisneverimaginedthat250yearsago.

銆€銆€Ifagainplease11850throughto1980,Iheardthatabombcanflattenacity,thispersonmaybedirectlyscaredsilly,130yearsagotheNobelwasn'tinventeddynamite.

銆€銆€Thenplease1in1980peoplenow?Thispersonwillbecry?

銆€銆€浜哄伐鏅鸿兘鍒╁紛鐨勮嫳鏂囦綔鏂囩浜岀瘒

銆€銆€Theprogressofartificialintelligence.Speedisamazing,thefuturewewillstarttoworkside-by-sidewithartificialintelligence.

銆€銆€AlphaGofire,fiveonehundredmillionpeoplewatching"man-machinewar",intheenditdependsonthetechnicaladvantageofbigdataanddeeplearningina4-1winnersposturetellpeople,toartificial

銆€銆€intelligenceisnolongerjustthesceneinthemovie,butintherealworldthereisanotherroundofindustrialrevolution,however,thischangesmakemanypeoplefeelscared,atthattimeallkindsofartificialintelligencethreatstothehumanvoice,accordingtotheBritishscienceassociationentrustednetworkresearchfirmYouGov,accordingtoasurveyofabout36%ofpeoplethinkthattheriseofartificialintelligencetechnologywillposeathreattohumanlong-termsurvival.Peopleinall

銆€銆€kindsofartificialintelligencecanbringbigBob"unemployment"isdeeplyconcernedaboutthediscourse,butalsoinsuchatoughAlphaGowillbemalicioususeworryingonsuchissues.

銆€銆€浜哄伐鏅鸿兘鍒╁紛鐨勮嫳鏂囦綔鏂囩涓夌瘒

銆€銆€Futuretrendsincomputerscienceisoneoftheartificialintelligence锛孖tistheresearchandartificialsimulationofhumanthoughtand

銆€銆€eventuallybeabletomakeahumanliketothinkthesamemachine.Forhumanservicesandtohelppeoplesolveproblems.

銆€銆€Afterall,peoplethoughtitwasunique,therearefeelings,thereareavarietyofcharacter,thiswillbeverydifficulttoachieveinthemachine.Infact,todothesameasthehumanthinkingmachine,theonlyoneoftheartificialintelligence,isbynomeansall.Throughthestudyofartificialintelligence,canresolveallkindsofscientificproblems,andpromotethedevelopmentofotherscience,theartificialintelligenceisthebest!

銆€銆€Ibelievethatthescienceofartificialintelligenceiswaitingforhumanitytoexploreitstepbysteptherealconnotation.

銆€銆€鐚滀綘鍠滄锛?/p>

1.鏈夊叧浜哄伐鏅鸿兘鍒╁紛鐨勮嫳璇綔鏂?

2.瀵逛簬浜哄伐鏅鸿兘鐨勭湅娉曡嫳璇綔鏂?

3.浜哄伐鏅鸿兘鐨勫埄寮婇珮涓嫳璇綔鏂?

4.鍏充簬浜哄伐鏅鸿兘鐨勫奖鍝嶈嫳鏂囦綔鏂?

5.浜哄伐鏅鸿兘褰卞搷鐢熸椿鑻辫浣滄枃鑼冩枃

6.浜哄伐鏅鸿兘濡備綍褰卞搷鎴戜滑鐨勮嫳璇綔鏂?

7.浜哄伐鏅鸿兘鎬庝箞褰卞搷鐢熸椿鑻辫浣滄枃

8.浜哄伐鏅鸿兘鎬庢牱褰卞搷浜虹被鐨勮嫳璇綔鏂?

怎样看待人工智能英语作文

銆€銆€鎬庢牱鐪嬪緟浜哄伐鏅鸿兘鑻辫浣滄枃绡?

銆€銆€Canmachinesreallythink?Theartificialintelligence,suchasacomputerthatthinkslikeahumanbeingisscary.Isbuildingamachinethatthinkslikeahumanreallypossible?WeareeverclosertobuildinganAIthatthinkslikeahuman.Whenitcomestothisissues,differentpeopleofferdifferentviews,somepeoplethinkthatmachinehasfeelingslikehumanbeingsisinterestinganditmaybeabetterservertohuman;whiletheotherthinkitisdangerous,itmaycausesarevolt.

銆€銆€Peoplewhoapprovedofhumanfeelingsmachinethinkthatoncerobothasspecificfeelings,suchashappy,sad,anger,theymightbemorehumanize.Forexample,maybeinthefuturearobotnannywillreplacearealhumannanny,whoareworkmoreeffectiveandwithoutanycomplain.Iftheyhaverealemotion,theyaremoreperfect,andmorelikeacompanybutnotacoolmachine.

銆€銆€Peoplewhoagainsthumanrobotarguethatoncetherobotismoreintelligentthanwethink,thatmaybeagreattribulationtohumanbeings.Therehasapotentialrisksthatoncetherobotissmartenough,theymayunwillingtobehuman鈥檚serveranymore,theymaywanttobelegallycitizens,orevenworse,tobetheowneroftheworld.Itispossiblebecausetheyaresmartandtheyarestrongercomparewithhumanbeings.

銆€銆€Itisnotsurewhatwillhappeninthefuture,havingrobottoserveforhumanbeingsisagoodthing,buttheissueofartificialintelligenceisstillcontroversial.

銆€銆€鎬庢牱鐪嬪緟浜哄伐鏅鸿兘鑻辫浣滄枃绡?

銆€銆€Inrecentyears,AI(artificialintelligence)isubiquitous,maybeyoudidn'tnoticeitatall,butrecently,Google'sAlphaGodefeatedLeeSedol,theWorldGoChampion.Itmustcauseyourattention,meanwhile,themachine'ssweepingvictorieshaveonceagainmadeAIahottopic.Theimpactofartificialintelligenceonourlifeismainlyreflectedinfollowingaspects.

銆€銆€First,theimpactofAIonnaturalscience.Inmanysubjectswhichneedcomputers,AIhasanimportantposition,conversely,AIishelpfultotheformationofourownintelligence.Second,theimpactofAIoneconomy.AIintovariousfieldstogeneratehugebenefit,butitalsocausesthequestionofemployment.AsAIreplacedthehumaninmanyways,itleadstoahugechangeinasocialframework.ThelastoneistheimpactofAIonsociety,AIprovidedanewmodeltoourlife,becausemanydevelopersuseAItodevelopmoreinterestinggames,itmakesourlifecolorful.

銆€銆€AIisadouble-edgedsword,becausesomepeopleexpectAItobenefitmankindinmorefields,andsomeothersfearthatAIwilleventuallygetoutofcontrol.Butinmyview,ifwecanuseitverywell,itwillbringmoreconveniencestoourlife,nottolosecontrol.Notonlysobutalsocandeveloptechnology.

銆€銆€鎬庢牱鐪嬪緟浜哄伐鏅鸿兘鑻辫浣滄枃绡?

銆€銆€Artificialintelligence(ai)approach,someoneworriesaboutunemployment,somepeopleinthefuture,someoneinexploringbusinessopportunities,alsosomepeopleonthego.Beforediscussingthese,maybeweshouldconsidertheoutcomeofhumanbeings.

銆€銆€Onemightthinkaboutthistopictooexaggeration,Thefirstrecallwhathashappenedinthehistoryofmankindincrediblethings.

銆€銆€Incrediblethings,theneedtopleaseafewthroughtodecide.Weplease1wasborninthe0peoplebornintheyearofthe(handynasty)through1600A.D.(Mingdynasty),althoughspans1600years,butthemanmaybeonthelivesofpeoplearoundyouwon'tfeeltooexaggerated,justchangedafewdynasty,stillfacingtheloessbackandbusyday.Butifplease11600Britishpeoplethroughto1850intheUK,seethehugesteelmonstersonthewaterran,thispersonmaydirectlybefrightenurine,thisisneverimaginedthat250yearsago.

銆€銆€Ifagainplease11850throughto1980,Iheardthatabombcanflattenacity,thispersonmaybedirectlyscaredsilly,130yearsagotheNobelwasn'tinventeddynamite.

銆€銆€Thenplease1in1980peoplenow?Thispersonwillbecry?

銆€銆€鎬庢牱鐪嬪緟浜哄伐鏅鸿兘鑻辫浣滄枃绡?

銆€銆€Notlongago,it'sahottopicthattheworldchampionGoLiShishiwasdefeatedbyAlphaGoownedbyGoogle.Itmarkstheprogressofartificialintelligence.Butthereisasayinggoselikethis"Everycoinhasitstwosides.".Somepeopleexpressfavourablelyreceive,othersworryaboutthatAIwouldcausechaostohumans.Tothis,it'snotcomprehensiveenough.

銆€銆€Undoubtedly,advancedtechnologyhasbroughtmuchconveniencetous.Insomespecialworkenvironment,AIcanhelphumantogetthejobdone.Simultaneously,it'slikelytoleadtocertainunemployment.ButitisnotsaidthatAIwillreplaceorevendestroyhuman.Therearetworeasonstosupporttheview.Tostartwith,AlphaGohingesuponthepowerfulcomputingcapacityofthecomputerandrepetitivelogic.Ithasmerelyain-depthstudytodepartedinformation,anditswisdomonlyshowsinacertainfield.However,thesuperiorityofhumansistheexpectationsandimaginationforfuture,ratherthancognitiveandlearningabilities.Weshouldinnovateaudaciously.Bymeansofveryunusualstrategy,onecanovercomeAlphaGo.Furthermore,peoplecanunderstandorconvinceothersbycommunication.Infact,agooddealoftestsshowthatitcan'tpersuadethehumantodoanything.ItshouldbeconcernedthattheremightbeanoutlawusestheAItocommitcrimesinthefuture.

銆€銆€Fromwhathasbeendiscussedabove,IthinkweshouldreveretheAIinsteadofthreatened.Inthefuture,humanswillcooperatewithAItofinishwork.Itisconducivetopromotethedevelopmentofsocietymoreandmorequicklyandefficiently.

銆€銆€鎬庢牱鐪嬪緟浜哄伐鏅鸿兘鑻辫浣滄枃绡?

銆€銆€Nowadays,withtherapiddevelopmentofinformationtechnology,internetandelectroniccommercehavebeenverypopularinourdailylives.Forexample,itisfashionableforyoungsterstopurchasedailyessentials,suchasbooks,clothes,electricalequipment,onsomefamouswebsite,likeTaobao,EBayandAlibaba,throughmanycouriercompanies.Asweallknown,onlineshoppinghasmanyadvantages.Firstly,onlineshoppingismoreconvenientthantraditionalmeans.Wecanfindashopwithsomanygoodsthatwemayfavor,whileallthesejustneedclickingourmouseandtyping-inthekeywordofwhatwewanttofind.Anditalsosavesouragreatsomeoftime.Secondly,morechoicesthanrealstoreareanotherattractiontocustomers.Onlineshoppingcanprovidemassinformationaboutproductswhichcanbesuitforcustomer'sneeds,tastes,andpreferences.Thirdly,aswithouttraditionalwarehousesandretailshops,onlineshoppinghascanmakeusgainlowercostsandprices.However,inspiteofitsadvantages,wecan'tturnablindeyetoitsdisadvantages.Obviously,qualityproblemisitsfirstdisadvantage.

銆€銆€Customersalwaysbuyfakecommoditieswhicharenotdescribedasonlineshops.Inaddition,it'stroublesomeandannoyingforustomakeachangewhentheyarenotsatisfiedwithwhatweboughtonline.Theseconddisadvantageissecurityissues.Whenweshoponline,weneedpayforthecommoditiesbyelectronicpayments,buthackerscaninvadeourcomputersandstealourinformation,thisisnotsafeforonlineshopping.

銆€銆€鎬庢牱鐪嬪緟浜哄伐鏅鸿兘鑻辫浣滄枃绡?

銆€銆€Nowadays,withtherapiddevelopmentofinformationtechnology,internetandelectroniccommercehavebeenverypopularinourdailylives.Forexample,itisfashionableforyoungsterstopurchasedailyessentials,suchasbooks,clothes,electricalequipment,onsomefamouswebsite,like

銆€銆€Taobao,EBayandAlibaba,throughmanycouriercompanies.Asweallknown,onlineshoppinghasmanyadvantages.Firstly,onlineshoppingismoreconvenientthantraditionalmeans.Wecanfindashopwithsomanygoodsthatwemayfavor,whileallthesejustneedclickingourmouseandtyping-inthekeywordofwhatwewanttofind.Anditalsosavesouragreatsomeoftime.Secondly,morechoicesthanrealstoreareanotherattractiontocustomers.Onlineshoppingcanprovidemass

銆€銆€informationaboutproductswhichcanbesuitforcustomer'sneeds,tastes,andpreferences.Thirdly,aswithouttraditionalwarehousesandretailshops,onlineshoppinghascanmakeusgainlowercostsandprices.However,inspiteofitsadvantages,wecan'tturnablindeyetoits

銆€銆€disadvantages.Obviously,qualityproblemisitsfirstdisadvantage.

銆€銆€Customersalwaysbuyfakecommoditieswhicharenotdescribedasonlineshops.Inaddition,it'stroublesomeandannoyingforustomakeachangewhentheyarenotsatisfiedwithwhatweboughtonline.Thesecond

銆€銆€disadvantageissecurityissues.Whenweshoponline,weneedpayforthecommoditiesbyelectronicpayments,buthackerscaninvadeour

銆€銆€computersandstealourinformation,thisisnotsafeforonlineshopping.

銆€銆€鎬庢牱鐪嬪緟浜哄伐鏅鸿兘鑻辫浣滄枃绡?

銆€銆€Futuretrendsincomputerscienceisoneoftheartificialintelligence锛孖tistheresearchandartificialsimulationofhumanthoughtandeventuallybeabletomakeahumanliketothinkthesamemachine.Forhumanservicesandtohelppeoplesolveproblems.

銆€銆€Afterall,peoplethoughtitwasunique,therearefeelings,thereareavarietyofcharacter,thiswillbeverydifficulttoachieveinthemachine.Infact,todothesameasthehumanthinkingmachine,theonlyoneoftheartificialintelligence,isbynomeansall.Throughthestudyofartificialintelligence,canresolveallkindsofscientificproblems,andpromotethedevelopmentofotherscience,theartificialintelligenceisthebest!

銆€銆€Ibelievethatthescienceofartificialintelligenceiswaitingforhumanitytoexploreitstepbysteptherealconnotation.

人工智能一些术语总结

闅忕潃鏅鸿兘鏃朵唬鎱㈡參鐨勫埌鏉ワ紝鏈変竴浜涘熀鏈蹇甸兘涓嶇煡閬撶湡鐨勬槸瑕佽惤浼嶄簡锛屼綔涓烘鍦ㄧН鏋佸涔犲悜涓婄殑闈掑勾锛屾垜鎯虫€荤粨涓€浠界瑪璁帮紝姝や唤绗旇浼氳褰曚紬澶欰I棰嗗煙鐨勬湳璇拰姒傚康锛屽綋鐒讹紝瀛︿竴閮ㄥ垎璁板綍涓€閮ㄥ垎锛屽苟涓斿彲鑳戒細澶规潅鐫€鑷繁鐨勪竴浜涚悊瑙ｏ紝鐢变簬鑳藉姏鏈夐檺锛屾湁闂甯屾湜澶у澶氬璧愭暀銆傚綋鐒讹紝鐢变簬鍐呭澶锛屼粎浠呭彧鏄褰曚簡涓嫳鍚嶅鐓э紝鏈夌殑鍔犱笂浜嗙畝鍗曠殑瑙ｉ噴锛屾病鍔犵殑鍚庣画澶у鏈夐渶姹傦紝鎴戜細鎱㈡參瀹屽杽~~銆傜洰褰曟殏瀹氫互棣栧瓧姣嶇殑瀛楀吀搴忔帓搴忋€傚彲浠ュ綋浣滅洰褰曟柟渚夸互鍚庢煡闃厏~寤鸿鏀惰棌鍔犵偣璧炲搱鍝堝搱

------------------------------------------------杩欓噷鏄垎鍓茬嚎--------------------------------------------------

A

鍑嗙‘鐜囷紙accuracy锛?

鍒嗙被妯″瀷棰勬祴鍑嗙‘鐨勬瘮渚嬨€?br/>

浜屽垎绫婚棶棰樹腑锛屽噯纭巼瀹氫箟涓猴細accuracy=(truepositives+truenegatives)/allsamples

澶氬垎绫婚棶棰樹腑锛屽噯纭巼瀹氫箟涓猴細accuracy=correctpredictions/allsamples

婵€娲诲嚱鏁帮紙activationfunction锛?

涓€绉嶅嚱鏁帮紝灏嗗墠涓€灞傛墍鏈夌缁忓厓婵€娲诲€肩殑鍔犳潈鍜?杈撳叆鍒颁竴涓潪绾挎€у嚱鏁颁腑锛岀劧鍚庝綔涓轰笅涓€灞傜缁忓厓鐨勮緭鍏ワ紝渚嬪ReLU鎴?Sigmoid

AdaGrad

涓€绉嶅鏉傜殑姊害涓嬮檷绠楁硶锛岄噸鏂拌皟鑺傛瘡涓弬鏁扮殑姊害锛岄珮鏁堝湴缁欐瘡涓弬鏁颁竴涓崟鐙殑瀛︿範鐜囥€?br/>

AUC锛堟洸绾夸笅闈㈢Н锛?

涓€绉嶈€冭檻鍒版墍鏈夊彲鑳界殑鍒嗙被闃堝€肩殑璇勪及鏍囧噯銆俁OC鏇茬嚎涓嬮潰绉唬琛ㄥ垎绫诲櫒闅忔満棰勬祴鐪熸绫伙紙TurePositives锛夎姣斿亣姝ｇ被锛團alsePositives锛夋鐜囧ぇ鐨勭‘淇″害銆?br/>

Adversarialexample锛堝鎶楁牱鏈級

AdversarialNetworks锛堝鎶楃綉缁滐級

ArtificialGeneralIntelligence/AGI锛堥€氱敤浜哄伐鏅鸿兘锛?

Attentionmechanism锛堟敞鎰忓姏鏈哄埗锛?

Autoencoder锛堣嚜缂栫爜鍣級

Automaticsummarization锛堣嚜鍔ㄦ憳瑕侊級

Averagegradient锛堝钩鍧囨搴︼級

Average-Pooling锛堝钩鍧囨睜鍖栵級

B

鍙嶅悜浼犳挱锛圔ackpropagation/BP锛?

绁炵粡缃戠粶涓畬鎴愭搴︿笅闄嶇殑閲嶈绠楁硶銆傞鍏堬紝鍦ㄥ墠鍚戜紶鎾殑杩囩▼涓绠楁瘡涓妭鐐圭殑杈撳嚭鍊笺€傜劧鍚庯紝鍦ㄥ弽鍚戜紶鎾殑杩囩▼涓绠椾笌姣忎釜鍙傛暟瀵瑰簲鐨勮宸殑鍋忓鏁般€?br/>

鍩虹嚎锛圔aseline锛?

琚敤涓哄姣旀ā鍨嬭〃鐜板弬鑰冪殑绠€鍗曟ā鍨嬨€?br/>

鎵归噺锛圔atch锛?

妯″瀷璁粌涓竴涓凯浠ｏ紙鎸囦竴娆℃搴︽洿鏂帮級浣跨敤鐨勬牱鏈泦銆?br/>

鎵归噺澶у皬锛圔atchsize锛?

涓€涓壒閲忎腑鏍锋湰鐨勬暟閲忋€備緥濡傦紝SGD鐨勬壒閲忓ぇ灏忎负1锛岃€?mini-batch鐨勬壒閲忓ぇ灏忛€氬父鍦?10-1000涔嬮棿銆?br/>

鍋忕疆锛圔ias锛?

涓庡師鐐圭殑鎴窛鎴栧亸绉婚噺銆?br/>

浜屽厓鍒嗙被鍣紙Binaryclassification锛?

涓€绫诲垎绫讳换鍔★紝杈撳嚭涓や釜浜掓枼绫诲埆涓殑涓€涓€傛瘮濡傚瀮鍦鹃偖浠舵娴嬨€?br/>

璇嶈锛圔agofwords/Bow锛?

鍩哄涔犲櫒锛圔aselearner锛?

鍩哄涔犵畻娉曪紙Baselearningalgorithm锛?

璐濆彾鏂綉缁滐紙Bayesiannetwork锛?

鍩哄噯锛圔echmark锛?

淇″康缃戠粶锛圔eliefnetwork锛?

浜岄」鍒嗗竷锛圔inomialdistribution锛?

鐜诲皵鍏规浖鏈猴紙Boltzmannmachine锛?

鑷姪閲囨牱娉曪紡鍙噸澶嶉噰鏍凤紡鏈夋斁鍥為噰鏍凤紙Bootstrapsampling锛?

骞挎挱锛圔roadcasting锛?

C

绫诲埆锛圕lass锛?

鎵€鏈夊悓绫诲睘鎬х殑鐩爣鍊间綔涓轰竴涓爣绛俱€?br/>

鍒嗙被妯″瀷锛坈lassification锛?

鏈哄櫒瀛︿範妯″瀷鐨勪竴绉嶏紝灏嗘暟鎹垎绂讳负涓や釜鎴栧涓鏁ｇ被鍒€?br/>

鏀舵暃锛坈onvergence锛?

璁粌杩囩▼杈惧埌鐨勬煇绉嶇姸鎬侊紝鍏朵腑璁粌鎹熷け鍜岄獙璇佹崯澶卞湪缁忚繃浜嗙‘瀹氱殑杩唬娆℃暟鍚庯紝鍦ㄦ瘡涓€娆¤凯浠ｄ腑锛屾敼鍙樺緢灏忔垨瀹屽叏涓嶅彉銆?br/>

鍑稿嚱鏁帮紙concexfunction锛?

涓€绉嶅舰鐘跺ぇ鑷村憟瀛楁瘝 U褰㈡垨纰楀舰鐨勫嚱鏁般€傜劧鑰岋紝鍦ㄩ€€鍖栨儏褰腑锛屽嚫鍑芥暟鐨勫舰鐘跺氨鍍忎竴鏉＄嚎銆?br/>

鎴愭湰锛坈ost锛?

loss鐨勫悓涔夎瘝銆傛繁搴﹀涔犳ā鍨嬩竴鑸兘浼氬畾涔夎嚜宸辩殑loss鍑芥暟銆?br/>

浜ゅ弶鐔碉紙cross-entropy锛?

澶氱被鍒垎绫婚棶棰樹腑瀵?Log鎹熷け鍑芥暟鐨勬帹骞裤€備氦鍙夌喌閲忓寲涓や釜姒傜巼鍒嗗竷涔嬮棿鐨勫尯鍒€?br/>

鏉′欢鐔碉紙Conditionalentropy锛?

鏉′欢闅忔満鍦猴紙Conditionalrandomfield/CRF锛?

缃俊搴︼紙Confidence锛?

鍏辫江鏂瑰悜(Conjugatedirections)

鍏辫江鍒嗗竷(Conjugatedistribution)

鍏辫江姊害(Conjugategradient)

鍗风Н绁炵粡缃戠粶锛圕onvolutionalneuralnetwork/CNN锛?

浣欏鸡鐩镐技搴︼紙Cosinesimilarity锛?

鎴愭湰鍑芥暟锛圕ostFunction锛?

鏇茬嚎鎷熷悎锛圕urve-fitting锛?

D

鏁版嵁闆嗭紙dataset锛?

鏍锋湰鐨勯泦鍚?br/>

娣卞害妯″瀷锛坉eepmodel锛?

涓€绉嶅寘鍚涓殣钘忓眰鐨勭缁忕綉缁溿€傛繁搴︽ā鍨嬩緷璧栦簬鍏跺彲璁粌鐨勯潪绾挎€ф€ц川銆傚拰瀹藉害妯″瀷瀵圭収锛坵idemodel锛夈€?br/>

dropout姝ｅ垯鍖栵紙dropoutregularization锛?

璁粌绁炵粡缃戠粶鏃朵竴绉嶆湁鐢ㄧ殑姝ｅ垯鍖栨柟娉曘€俤ropout姝ｅ垯鍖栫殑杩囩▼鏄湪鍗曟姊害璁＄畻涓垹鍘讳竴灞傜綉缁滀腑闅忔満閫夊彇鐨勫浐瀹氭暟閲忕殑鍗曞厓銆傚垹鍘荤殑鍗曞厓瓒婂锛屾鍒欏寲瓒婂己銆?br/>

鏁版嵁鎸栨帢锛圖atamining锛?

鍐崇瓥鏍?鍒ゅ畾鏍戯紙Decisiontree锛?

娣卞害绁炵粡缃戠粶锛圖eepneuralnetwork/DNN锛?

鐙勫埄鍏嬮浄鍒嗗竷锛圖irichletdistribution锛?

鍒ゅ埆妯″瀷锛圖iscriminativemodel锛?

涓嬮噰鏍凤紙Downsampling锛?

鍔ㄦ€佽鍒掞紙Dynamicprogramming锛?

E

鏃╂湡鍋滄娉曪紙earlystopping锛?

涓€绉嶆鍒欏寲鏂规硶锛屽湪璁粌鎹熷け瀹屾垚涓嬮檷涔嬪墠鍋滄妯″瀷璁粌杩囩▼銆傚綋楠岃瘉鏁版嵁闆嗭紙validationdataset锛夌殑鎹熷け寮€濮嬩笂鍗囩殑鏃跺€欙紝鍗虫硾鍖栬〃鐜板彉宸殑鏃跺€欙紝灏辫浣跨敤鏃╂湡鍋滄娉曚簡銆?br/>

宓屽叆锛坋mbeddings锛?

涓€绫昏〃绀轰负杩炵画鍊肩壒寰佺殑鏄庣‘鐨勭壒寰併€傚祵鍏ラ€氬父鎸囧皢楂樼淮鍚戦噺杞崲鍒颁綆缁寸┖闂翠腑銆?br/>

缁忛獙椋庨櫓鏈€灏忓寲锛坋mpiricalriskminimization锛孍RM锛?

閫夋嫨鑳戒娇寰楄缁冩暟鎹殑鎹熷け鍑芥暟鏈€灏忓寲鐨勬ā鍨嬬殑杩囩▼銆傚拰缁撴瀯椋庨櫓鏈€灏忓寲锛坰tructualriskminimization锛夊鐓с€?br/>

闆嗘垚锛坋nsemble锛?

澶氫釜妯″瀷棰勬祴鐨勭患鍚堣€冭檻銆傚彲浠ラ€氳繃浠ヤ笅涓€绉嶆垨鍑犵鏂规硶鍒涘缓涓€涓泦鎴愭柟娉曪細

璁剧疆涓嶅悓鐨勫垵濮嬪寲锛?br/>

璁剧疆涓嶅悓鐨勮秴鍙傞噺锛?br/>

璁剧疆涓嶅悓鐨勬€讳綋缁撴瀯銆?br/>

娣卞害鍜屽箍搴︽ā鍨嬫槸涓€绉嶉泦鎴愩€?br/>

鏍锋湰锛坋xample锛?

涓€涓暟鎹泦鐨勪竴琛屽唴瀹广€備竴涓牱鏈寘鍚簡涓€涓垨澶氫釜鐗瑰緛锛屼篃鍙兘鏄竴涓爣绛俱€傚弬瑙佹爣娉ㄦ牱鏈紙labeledexample锛夊拰鏃犳爣娉ㄦ牱鏈紙unlabeledexample锛夈€?br/>

F

鍋囪礋绫伙紙falsenegative锛孎N锛?

琚ā鍨嬮敊璇殑棰勬祴涓鸿礋绫荤殑鏍锋湰銆備緥濡傦紝妯″瀷鎺ㄦ柇涓€灏侀偖浠朵负闈炲瀮鍦鹃偖浠讹紙璐熺被锛夛紝浣嗗疄闄呬笂杩欏皝閭欢鏄瀮鍦鹃偖浠躲€?br/>

鍋囨绫伙紙falsepositive锛孎P锛?

琚ā鍨嬮敊璇殑棰勬祴涓烘绫荤殑鏍锋湰銆備緥濡傦紝妯″瀷鎺ㄦ柇涓€灏侀偖浠朵负鍨冨溇閭欢锛堟绫伙級锛屼絾瀹為檯涓婅繖灏侀偖浠舵槸闈炲瀮鍦鹃偖浠躲€?br/>

鍋囨绫荤巼锛坒alsepositiverate锛孎Prate锛?

ROC鏇茬嚎锛圧OCcurve锛変腑鐨?x杞淬€侳P鐜囩殑瀹氫箟鏄細鍋囨鐜?鍋囨绫绘暟/(鍋囨绫绘暟+鐪熻礋绫绘暟)

鐗瑰緛宸ョ▼锛坒eatureengineering锛?

鍦ㄨ缁冩ā鍨嬬殑鏃跺€欙紝鎸栨帢瀵规ā鍨嬫晥鏋滄湁鍒╃殑鐗瑰緛銆?br/>

鍓嶉绁炵粡缃戠粶锛團eedforwardNeuralNetworks/FNN锛?

G

娉涘寲锛坓eneralization锛?

鎸囨ā鍨嬪埄鐢ㄦ柊鐨勬病瑙佽繃鐨勬暟鎹€屼笉鏄敤浜庤缁冪殑鏁版嵁浣滃嚭姝ｇ‘鐨勯娴嬬殑鑳藉姏銆?br/>

骞夸箟绾挎€фā鍨嬶紙generalizedlinearmodel锛?

鏈€灏忎簩涔樺洖褰掓ā鍨嬬殑鎺ㄥ箍/娉涘寲锛屽熀浜庨珮鏂櫔澹帮紝鐩稿浜庡叾瀹冪被鍨嬬殑妯″瀷锛堝熀浜庡叾瀹冪被鍨嬬殑鍣０锛屾瘮濡傛硦鏉惧櫔澹帮紝鎴栫被鍒櫔澹帮級銆傚箍涔夌嚎鎬фā鍨嬬殑渚嬪瓙鍖呮嫭锛?br/>

logistic鍥炲綊

澶氬垎绫诲洖褰?br/>

鏈€灏忎簩涔樺洖褰?br/>

姊害锛坓radient锛?

鎵€鏈夊彉閲忕殑鍋忓鏁扮殑鍚戦噺銆傚湪鏈哄櫒瀛︿範涓紝姊害鏄ā鍨嬪嚱鏁扮殑鍋忓鏁板悜閲忋€傛搴︽寚鍚戞渶闄″抄鐨勪笂鍗囪矾绾裤€?br/>

姊害鎴柇锛坓radientclipping锛?

鍦ㄥ簲鐢ㄦ搴︿箣鍓嶅厛淇グ鏁板€硷紝姊害鎴柇鏈夊姪浜庣‘淇濇暟鍊肩ǔ瀹氭€э紝闃叉姊害鐖嗙偢鍑虹幇銆?br/>

姊害涓嬮檷锛坓radientdescent锛?

閫氳繃璁＄畻妯″瀷鐨勭浉鍏冲弬閲忓拰鎹熷け鍑芥暟鐨勬搴︽渶灏忓寲鎹熷け鍑芥暟锛屽€煎彇鍐充簬璁粌鏁版嵁銆傛搴︿笅闄嶈凯浠ｅ湴璋冩暣鍙傞噺锛岄€愭笎闈犺繎鏉冮噸鍜屽亸缃殑鏈€浣崇粍鍚堬紝浠庤€屾渶灏忓寲鎹熷け鍑芥暟銆?br/>

鍥撅紙graph锛?

鍦?TensorFlow涓殑涓€绉嶈绠楄繃绋嬪睍绀恒€傚浘涓殑鑺傜偣琛ㄧず鎿嶄綔銆傝妭鐐圭殑杩炵嚎鏄湁鎸囧悜鎬х殑锛岃〃绀轰紶閫掍竴涓搷浣滐紙涓€涓紶閲忥級鐨勭粨鏋滐紙浣滀负涓€涓搷浣滄暟锛夌粰鍙︿竴涓搷浣溿€備娇鐢?TensorBoard鑳藉彲瑙嗗寲璁＄畻鍥俱€?br/>

楂樻柉鏍稿嚱鏁帮紙Gaussiankernelfunction锛?

楂樻柉娣峰悎妯″瀷锛圙aussianMixtureModel锛?

楂樻柉杩囩▼锛圙aussianProcess锛?

娉涘寲璇樊锛圙eneralizationerror锛?

鐢熸垚妯″瀷锛圙enerativeModel锛?

閬椾紶绠楁硶锛圙eneticAlgorithm/GA锛?

鍚夊竷鏂噰鏍凤紙Gibbssampling锛?

鍩哄凹鎸囨暟锛圙iniindex锛?

姊害涓嬮檷锛圙radientDescent锛?

H

鍚彂寮忥紙heuristic锛?

涓€涓棶棰樼殑瀹為檯鐨勫拰闈炴渶浼樼殑瑙ｏ紝浣嗚兘浠庡涔犵粡楠屼腑鑾峰緱瓒冲澶氱殑杩涙銆?br/>

闅愯棌灞傦紙hiddenlayer锛?

绁炵粡缃戠粶涓綅浜庤緭鍏ュ眰锛堝嵆鐗瑰緛锛夊拰杈撳嚭灞傦紙鍗抽娴嬶級涔嬮棿鐨勫悎鎴愬眰銆備竴涓缁忕綉缁滃寘鍚竴涓垨澶氫釜闅愯棌灞傘€?br/>

瓒呭弬鏁帮紙hyperparameter锛?

杩炵画璁粌妯″瀷鐨勮繃绋嬩腑鍙互鎷у姩鐨勩€屾棆閽€嶃€備緥濡傦紝鐩稿浜庢ā鍨嬭嚜鍔ㄦ洿鏂扮殑鍙傛暟锛屽涔犵巼锛坙earningrate锛夋槸涓€涓秴鍙傛暟銆傚拰鍙傞噺瀵圭収銆?br/>

纭棿闅旓紙Hard margin锛?

闅愰┈灏斿彲澶ā鍨嬶紙HiddenMarkovModel/HMM锛?

灞傛鑱氱被锛圚ierarchicalclustering锛?

鍋囪妫€楠岋紙Hypothesistest锛?

I

鐙珛鍚屽垎甯冿紙independentlyandidenticallydistributed锛宨.i.d锛?

浠庝笉浼氭敼鍙樼殑鍒嗗竷涓幏鍙栫殑鏁版嵁锛屼笖鑾峰彇鐨勬瘡涓€间笉渚濊禆浜庝箣鍓嶈幏鍙栫殑鍊笺€俰.i.d.鏄満鍣ㄥ涔犵殑鐞嗘兂鎯呭喌鈥斺€斾竴绉嶆湁鐢ㄤ絾鍦ㄧ幇瀹炰笘鐣屼腑鍑犱箮鎵句笉鍒扮殑鏁板鏋勫缓銆?br/>

鎺ㄦ柇锛坕nference锛?

鍦ㄦ満鍣ㄥ涔犱腑锛岄€氬父鎸囧皢璁粌妯″瀷搴旂敤鍒版棤鏍囨敞鏍锋湰鏉ヨ繘琛岄娴嬬殑杩囩▼銆傚湪缁熻瀛︿腑锛屾帹鏂寚鍦ㄨ瀵熷埌鐨勬暟鎹殑鍩虹涓婃嫙鍚堝垎甯冨弬鏁扮殑杩囩▼銆?br/>

杈撳叆灞傦紙inputlayer锛?

绁炵粡缃戠粶鐨勭涓€灞傦紙鎺ユ敹杈撳叆鏁版嵁锛夈€?br/>

璇勫垎鑰呴棿涓€鑷存€э紙inter-rateragreement锛?

鐢ㄦ潵琛￠噺涓€椤逛换鍔′腑浜虹被璇勫垎鑰呮剰瑙佷竴鑷寸殑鎸囨爣銆傚鏋滄剰瑙佷笉涓€鑷达紝鍒欎换鍔¤鏄庡彲鑳介渶瑕佹敼杩涖€傛湁鏃朵篃鍙爣娉ㄨ€呴棿淇″害锛坕nter-annotatoragreement锛夋垨璇勫垎鑰呴棿淇″害锛坕nter-raterreliability锛夈€?br/>

澧為噺瀛︿範锛圛ncrementallearning锛?

鐙珛鎴愬垎鍒嗘瀽锛圛ndependentComponentAnalysis/ICA锛?

鐙珛瀛愮┖闂村垎鏋愶紙Independentsubspaceanalysis锛?

淇℃伅鐔碉紙Informationentropy锛?

淇℃伅澧炵泭锛圛nformationgain锛?

J

JS鏁ｅ害锛圝ensen-ShannonDivergence/JSD锛?

K

Kernel鏀寔鍚戦噺鏈猴紙KernelSupportVectorMachines/KSVM锛?

涓€绉嶅垎绫荤畻娉曪紝鏃ㄥ湪閫氳繃灏嗚緭鍏ユ暟鎹悜閲忔槧灏勫埌鏇撮珮缁村害鐨勭┖闂翠娇姝ｇ被鍜岃礋绫讳箣闂寸殑杈归檯鏈€澶у寲銆備緥濡傦紝鑰冭檻涓€涓緭鍏ユ暟鎹泦鍖呭惈涓€鐧句釜鐗瑰緛鐨勫垎绫婚棶棰樸€備负浜嗕娇姝ｇ被鍜岃礋绫讳箣闂寸殑闂撮殧鏈€澶у寲锛孠SVM浠庡唴閮ㄥ皢鐗瑰緛鏄犲皠鍒扮櫨涓囩淮搴︾殑绌洪棿銆侹SVM浣跨敤鐨勬崯澶卞嚱鏁板彨浣?hinge鎹熷け銆?br/>

鏍告柟娉曪紙Kernelmethod锛?

鏍告妧宸э紙Kerneltrick锛?

k鎶樹氦鍙夐獙璇侊紡k鍊嶄氦鍙夐獙璇侊紙K-foldcrossvalidation锛?

K-鍧囧€艰仛绫伙紙K-MeansClustering锛?

K杩戦偦绠楁硶锛圞-NearestNeighboursAlgorithm/KNN锛?

鐭ヨ瘑鍥捐氨锛圞nowledgegraph锛?

鐭ヨ瘑搴擄紙Knowledgebase锛?

鐭ヨ瘑琛ㄥ緛锛圞nowledgeRepresentation锛?

L

L1鎹熷け鍑芥暟锛圠1loss锛?

鎹熷け鍑芥暟鍩轰簬妯″瀷瀵规爣绛剧殑棰勬祴鍊煎拰鐪熷疄鍊肩殑宸殑缁濆鍊艰€屽畾涔夈€侺1鎹熷け鍑芥暟姣旇捣L2鎹熷け鍑芥暟瀵瑰紓甯稿€肩殑鏁忔劅搴︽洿灏忋€?br/>

L1姝ｅ垯鍖栵紙L1regularization锛?

涓€绉嶆鍒欏寲锛屾寜鐓ф潈閲嶇粷瀵瑰€兼€诲拰鐨勬瘮渚嬭繘琛屾儵缃氥€傚湪渚濊禆绋€鐤忕壒寰佺殑妯″瀷涓紝L1姝ｅ垯鍖栧府鍔╀績浣匡紙鍑犱箮锛変笉鐩稿叧鐨勭壒寰佺殑鏉冮噸瓒嬭繎浜?0锛屼粠鑰屼粠妯″瀷涓Щ闄よ繖浜涚壒寰併€?br/>

L2鎹熷け锛圠2loss锛?

鍙傝骞虫柟鎹熷け銆?br/>

L2姝ｅ垯鍖栵紙L2regularization锛?

涓€绉嶆鍒欏寲锛屾寜鐓ф潈閲嶅钩鏂圭殑鎬诲拰鐨勬瘮渚嬭繘琛屾儵缃氥€侺2姝ｅ垯鍖栧府鍔╀績浣垮紓甯稿€兼潈閲嶆洿鎺ヨ繎 0鑰屼笉瓒嬭繎浜?0銆傦紙鍙笌L1姝ｅ垯鍖栧鐓ч槄璇汇€傦級L2姝ｅ垯鍖栭€氬父鏀瑰杽绾挎€фā鍨嬬殑娉涘寲鏁堟灉銆?br/>

鏍囩锛坙abel锛?

鍦ㄧ洃鐫ｅ紡瀛︿範涓紝鏍锋湰鐨勩€岀瓟妗堛€嶆垨銆岀粨鏋溿€嶃€傛爣娉ㄦ暟鎹泦涓殑姣忎釜鏍锋湰鍖呭惈涓€鎴栧涓壒寰佸拰涓€涓爣绛俱€傚湪鍨冨溇閭欢妫€娴嬫暟鎹泦涓紝鐗瑰緛鍙兘鍖呮嫭涓婚銆佸彂鍑鸿€呬綍閭欢鏈韩锛岃€屾爣绛惧彲鑳芥槸銆屽瀮鍦鹃偖浠躲€嶆垨銆岄潪鍨冨溇閭欢銆嶃€?br/>

鏍囨敞鏍锋湰锛坙abeledexample锛?

鍖呭惈鐗瑰緛鍜屾爣绛剧殑鏍锋湰銆傚湪鐩戠潱寮忚缁冧腑锛屾ā鍨嬩粠鏍囨敞鏍锋湰涓繘琛屽涔犮€?br/>

瀛︿範鐜囷紙learningrate锛?

閫氳繃姊害涓嬮檷璁粌妯″瀷鏃朵娇鐢ㄧ殑涓€涓爣閲忋€傛瘡娆¤凯浠ｄ腑锛屾搴︿笅闄嶇畻娉曚娇瀛︿範鐜囦箻浠ユ搴︼紝涔樼Н鍙綔gradientstep銆傚涔犵巼鏄竴涓噸瑕佺殑瓒呭弬鏁般€?br/>

鏈€灏忎簩涔樺洖褰掞紙leastsquaresregression锛?

閫氳繃L2鎹熷け鏈€灏忓寲杩涜璁粌鐨勭嚎鎬у洖褰掓ā鍨嬨€?br/>

绾挎€у洖褰掞紙linearregression锛?

瀵硅緭鍏ョ壒寰佺殑绾挎€ц繛鎺ヨ緭鍑鸿繛缁€肩殑涓€绉嶅洖褰掓ā鍨嬨€?br/>

logistic鍥炲綊锛坙ogisticregression锛?

灏?sigmoid鍑芥暟搴旂敤浜庣嚎鎬ч娴嬶紝鍦ㄥ垎绫婚棶棰樹腑涓烘瘡涓彲鑳界殑绂绘暎鏍囩鍊肩敓鎴愭鐜囩殑妯″瀷銆傚敖绠?logistic鍥炲綊甯哥敤浜庝簩鍏冨垎绫婚棶棰橈紝浣嗗畠涔熺敤浜庡绫诲埆鍒嗙被闂锛堣繖绉嶆儏鍐典笅锛宭ogistic鍥炲綊鍙綔銆屽绫诲埆logistic鍥炲綊銆嶆垨銆屽椤瑰紡鍥炲綊銆嶃€?br/>

瀵规暟鎹熷け鍑芥暟锛圠ogLoss锛?

浜屽厓logistic鍥炲綊妯″瀷涓娇鐢ㄧ殑鎹熷け鍑芥暟銆?br/>

鎹熷け锛圠oss锛?

搴﹂噺妯″瀷棰勬祴涓庢爣绛捐窛绂荤殑鎸囨爣锛屽畠鏄害閲忎竴涓ā鍨嬫湁澶氱碂绯曠殑鎸囨爣銆備负浜嗙‘瀹氭崯澶卞€硷紝妯″瀷蹇呴』瀹氫箟鎹熷け鍑芥暟銆備緥濡傦紝绾挎€у洖褰掓ā鍨嬮€氬父浣跨敤鍧囨柟宸綔涓烘崯澶卞嚱鏁帮紝鑰?logistic鍥炲綊妯″瀷浣跨敤瀵规暟鎹熷け鍑芥暟銆?br/>

闅愮媱鍒╁厠闆峰垎甯冿紙LatentDirichletAllocation/LDA锛?

娼滃湪璇箟鍒嗘瀽锛圠atentsemanticanalysis锛?

绾挎€у垽鍒紙LinearDiscriminantAnalysis/LDA锛?

闀跨煭鏈熻蹇嗭紙Long-ShortTermMemory/LSTM锛?

M

鏈哄櫒瀛︿範锛坢achinelearning锛?

鍒╃敤杈撳叆鏁版嵁鏋勫缓锛堣缁冿級棰勬祴妯″瀷鐨勯」鐩垨绯荤粺銆傝绯荤粺浣跨敤瀛︿範鐨勬ā鍨嬪涓庤缁冩暟鎹浉鍚屽垎甯冪殑鏂版暟鎹繘琛屾湁鐢ㄧ殑棰勬祴銆傛満鍣ㄥ涔犺繕鎸囦笌杩欎簺椤圭洰鎴栫郴缁熺浉鍏崇殑鐮旂┒棰嗗煙銆?br/>

鍧囨柟璇樊锛圡eanSquaredError/MSE锛?

姣忎釜鏍锋湰鐨勫钩鍧囧钩鏂规崯澶便€侻SE鍙互閫氳繃骞虫柟鎹熷け闄や互鏍锋湰鏁伴噺鏉ヨ绠椼€?br/>

灏忔壒閲忥紙mini-batch锛?

鍦ㄨ缁冩垨鎺ㄦ柇鐨勪竴涓凯浠ｄ腑杩愯鐨勬暣鎵规牱鏈殑涓€涓皬鐨勯殢鏈洪€夋嫨鐨勫瓙闆嗐€傚皬鎵归噺鐨勫ぇ灏忛€氬父鍦?0鍒?1000涔嬮棿銆傚湪灏忔壒閲忔暟鎹笂璁＄畻鎹熷け姣斿湪鍏ㄩ儴璁粌鏁版嵁涓婅绠楁崯澶辫楂樻晥鐨勫銆?br/>

鏈哄櫒缈昏瘧锛圡achinetranslation/MT锛?

椹皵鍙か閾捐挋鐗瑰崱缃楁柟娉曪紙MarkovChainMonteCarlo/MCMC锛?

椹皵鍙か闅忔満鍦猴紙MarkovRandomField锛?

澶氭枃妗ｆ憳瑕侊紙Multi-documentsummarization锛?

澶氬眰鎰熺煡鍣紙MultilayerPerceptron/MLP锛?

澶氬眰鍓嶉绁炵粡缃戠粶锛圡ulti-layerfeedforwardneuralnetworks锛?

N

NaNtrap

璁粌杩囩▼涓紝濡傛灉妯″瀷涓殑涓€涓暟瀛楀彉鎴愪簡 NaN锛屽垯妯″瀷涓殑寰堝鎴栨墍鏈夊叾浠栨暟瀛楁渶缁堥兘鍙樻垚NaN銆侼aN鏄€孨otaNumber銆嶇殑缂╁啓銆?br/>

绁炵粡缃戠粶锛坣euralnetwork锛?

璇ユā鍨嬩粠澶ц剳涓幏鍙栫伒鎰燂紝鐢卞涓眰缁勬垚锛堝叾涓嚦灏戞湁涓€涓槸闅愯棌灞傦級锛屾瘡涓眰鍖呭惈绠€鍗曠殑杩炴帴鍗曞厓鎴栫缁忓厓锛屽叾鍚庢槸闈炵嚎鎬с€?br/>

绁炵粡鍏冿紙neuron锛?

绁炵粡缃戠粶涓殑鑺傜偣锛岄€氬父杈撳叆澶氫釜鍊硷紝鐢熸垚涓€涓緭鍑哄€笺€傜缁忓厓閫氳繃灏嗘縺娲诲嚱鏁帮紙闈炵嚎鎬ц浆鎹級搴旂敤鍒拌緭鍏ュ€肩殑鍔犳潈鍜屾潵璁＄畻杈撳嚭鍊笺€?br/>

褰掍竴鍖栵紙normalization锛?

灏嗗€肩殑瀹為檯鍖洪棿杞寲涓烘爣鍑嗗尯闂寸殑杩囩▼锛屾爣鍑嗗尯闂撮€氬父鏄?1鍒?1鎴?0鍒?1銆備緥濡傦紝鍋囪鏌愪釜鐗瑰緛鐨勮嚜鐒跺尯闂存槸800鍒?6000銆傞€氳繃鍑忔硶鍜屽垎鍓诧紝浣犲彲浠ユ妸閭ｄ簺鍊兼爣鍑嗗寲鍒板尯闂?1鍒?1銆傚弬瑙佺缉鏀俱€?br/>

Numpy

Python涓彁渚涢珮鏁堟暟缁勮繍绠楃殑寮€婧愭暟瀛﹀簱銆俻andas鍩轰簬numpy鏋勫缓銆?br/>

Naivebayes锛堟湸绱犺礉鍙舵柉锛?

NaiveBayesClassifier锛堟湸绱犺礉鍙舵柉鍒嗙被鍣級

Namedentityrecognition锛堝懡鍚嶅疄浣撹瘑鍒級

Naturallanguagegeneration/NLG锛堣嚜鐒惰瑷€鐢熸垚锛?

Naturallanguageprocessing锛堣嚜鐒惰瑷€澶勭悊锛?

Norm锛堣寖鏁帮級

O

鐩爣锛坥bjective锛?

绠楁硶灏濊瘯浼樺寲鐨勭洰鏍囧嚱鏁般€?br/>

one-hot缂栫爜锛堢嫭鐑紪鐮侊級锛坥ne-hotencoding锛?

涓€涓█鐤忓悜閲忥紝鍏朵腑锛氫竴涓厓绱犺缃负 1锛屾墍鏈夊叾浠栫殑鍏冪礌璁剧疆涓?0銆傘€?br/>

涓€瀵瑰锛坥ne-vs.-all锛?

缁欏嚭涓€涓湁 N涓彲鑳借В鍐虫柟妗堢殑鍒嗙被闂锛屼竴瀵瑰瑙ｅ喅鏂规鍖呮嫭N涓嫭绔嬬殑浜屽厓鍒嗙被鍣ㄢ€斺€旀瘡涓彲鑳界殑缁撴灉閮芥湁涓€涓簩鍏冨垎绫诲櫒銆備緥濡傦紝涓€涓ā鍨嬪皢鏍锋湰鍒嗕负鍔ㄧ墿銆佽敩鑿滄垨鐭跨墿锛屽垯涓€瀵瑰鐨勮В鍐虫柟妗堝皢鎻愪緵浠ヤ笅涓夌鐙珛鐨勪簩鍏冨垎绫诲櫒锛?br/>

鍔ㄧ墿鍜岄潪鍔ㄧ墿

钄彍鍜岄潪钄彍

鐭跨墿鍜岄潪鐭跨墿

杩囨嫙鍚堬紙overfitting锛?

鍒涘缓鐨勬ā鍨嬩笌璁粌鏁版嵁闈炲父鍖归厤锛屼互鑷充簬妯″瀷鏃犳硶瀵规柊鏁版嵁杩涜姝ｇ‘鐨勯娴?br/>

Oversampling锛堣繃閲囨牱锛?

P

pandas

涓€绉嶅熀浜庡垪鐨勬暟鎹垎鏋?API銆傚緢澶氭満鍣ㄥ涔犳鏋讹紝鍖呮嫭TensorFlow锛屾敮鎸?pandas鏁版嵁缁撴瀯浣滀负杈撳叆銆傚弬瑙?pandas鏂囨。銆?br/>

鍙傛暟锛坧arameter锛?

鏈哄櫒瀛︿範绯荤粺鑷璁粌鐨勬ā鍨嬬殑鍙橀噺銆備緥濡傦紝鏉冮噸鏄弬鏁帮紝瀹冪殑鍊兼槸鏈哄櫒瀛︿範绯荤粺閫氳繃杩炵画鐨勮缁冭凯浠ｉ€愭笎瀛︿範鍒扮殑銆傛敞鎰忎笌瓒呭弬鏁扮殑鍖哄埆銆?br/>

鎬ц兘锛坧erformance锛?

鍦ㄨ蒋浠跺伐绋嬩腑鐨勪紶缁熷惈涔夛細杞欢杩愯閫熷害鏈夊蹇紡楂樻晥锛?br/>

鍦ㄦ満鍣ㄥ涔犱腑鐨勫惈涔夛細妯″瀷鐨勫噯纭巼濡備綍锛熷嵆锛屾ā鍨嬬殑棰勬祴缁撴灉鏈夊濂斤紵

鍥版儜搴︼紙perplexity锛?

瀵规ā鍨嬪畬鎴愪换鍔＄殑绋嬪害鐨勪竴绉嶅害閲忔寚鏍囥€備緥濡傦紝鍋囪浣犵殑浠诲姟鏄槄璇荤敤鎴峰湪鏅鸿兘鎵嬫満涓婅緭鍏ョ殑鍗曡瘝鐨勫ご鍑犱釜瀛楁瘝锛屽苟鎻愪緵鍙兘鐨勫畬鏁村崟璇嶅垪琛ㄣ€傝浠诲姟鐨勫洶鎯戝害锛坧erplexity锛孭锛夋槸涓轰簡鍒楀嚭鍖呭惈鐢ㄦ埛瀹為檯鎯宠緭鍏ュ崟璇嶇殑鍒楄〃浣犻渶瑕佽繘琛岀殑鐚滄祴鏁伴噺銆?br/>

娴佺▼锛坧ipeline锛?

鏈哄櫒瀛︿範绠楁硶鐨勫熀纭€鏋舵瀯銆傜閬撳寘鎷敹闆嗘暟鎹€佸皢鏁版嵁鏀惧叆璁粌鏁版嵁鏂囦欢涓€佽缁冧竴鎴栧涓ā鍨嬶紝浠ュ強鏈€缁堣緭鍑烘ā鍨嬨€?br/>

Principalcomponentanalysis/PCA锛堜富鎴愬垎鍒嗘瀽锛?

Precision锛堟煡鍑嗙巼锛忓噯纭巼锛?

Priorknowledge锛堝厛楠岀煡璇嗭級

Q

QuasiNewtonmethod锛堟嫙鐗涢】娉曪級

R

鍙洖鐜囷紙recall锛?

鍥炲綊妯″瀷锛坮egressionmodel锛?

涓€绉嶈緭鍑烘寔缁€硷紙閫氬父鏄诞鐐规暟锛夌殑妯″瀷銆傝€屽垎绫绘ā鍨嬭緭鍑虹殑鏄鏁ｅ€笺€?br/>

姝ｅ垯鍖栵紙regularization锛?

瀵规ā鍨嬪鏉傚害鐨勬儵缃氥€傛鍒欏寲甯姪闃叉杩囨嫙鍚堛€傛鍒欏寲鍖呮嫭涓嶅悓绉嶇被锛?br/>

L1姝ｅ垯鍖?br/>

L2姝ｅ垯鍖?br/>

dropout姝ｅ垯鍖?br/>

earlystopping锛堣繖涓嶆槸姝ｅ紡鐨勬鍒欏寲鏂规硶锛屼絾鍙互楂樻晥闄愬埗杩囨嫙鍚堬級

姝ｅ垯鍖栫巼锛坮egularizationrate锛?

涓€绉嶆爣閲忕骇锛岀敤 lambda鏉ヨ〃绀猴紝鎸囨鍒欏嚱鏁扮殑鐩稿閲嶈鎬с€備粠涓嬮潰杩欎釜绠€鍖栫殑鎹熷け鍏紡鍙互鐪嬪嚭姝ｅ垯鍖栫巼鐨勪綔鐢細

minimize(lossfunction+位(regularizationfunction))

鎻愰珮姝ｅ垯鍖栫巼鑳藉闄嶄綆杩囨嫙鍚堬紝浣嗗彲鑳戒細浣挎ā鍨嬪噯纭巼闄嶄綆銆?br/>

琛ㄥ緛锛坮epresention锛?

灏嗘暟鎹槧灏勫埌鏈夌敤鐗瑰緛鐨勮繃绋嬨€?br/>

鍙楄瘯鑰呭伐浣滅壒寰佹洸绾匡紙receiveroperatingcharacteristic/ROCCurve锛?

鍙嶆槧鍦ㄤ笉鍚岀殑鍒嗙被闃堝€间笂锛岀湡姝ｇ被鐜囧拰鍋囨绫荤巼鐨勬瘮鍊肩殑鏇茬嚎銆傚弬瑙?AUC銆?br/>

RecurrentNeuralNetwork锛堝惊鐜缁忕綉缁滐級

Recursiveneuralnetwork锛堥€掑綊绁炵粡缃戠粶锛?

Reinforcementlearning/RL锛堝己鍖栧涔狅級

Re-sampling锛堥噸閲囨牱娉曪級

Representationlearning锛堣〃寰佸涔狅級

RandomForestAlgorithm锛堥殢鏈烘．鏋楃畻娉曪級

S

缂╂斁锛坰caling锛?

鐗瑰緛宸ョ▼涓父鐢ㄧ殑鎿嶄綔锛岀敤浜庢帶鍒剁壒寰佸€煎尯闂达紝浣夸箣涓庢暟鎹泦涓叾浠栫壒寰佺殑鍖洪棿鍖归厤銆備緥濡傦紝鍋囪浣犳兂浣挎暟鎹泦涓墍鏈夌殑娴偣鐗瑰緛鐨勫尯闂翠负 0鍒?1銆傜粰瀹氫竴涓壒寰佸尯闂存槸0鍒?500锛岄偅涔堜綘鍙互閫氳繃灏嗘瘡涓€奸櫎浠?500锛岀缉鏀剧壒寰佸€煎尯闂淬€傝繕鍙弬瑙佹鍒欏寲銆?br/>

scikit-learn

涓€绉嶆祦琛岀殑寮€婧愭満鍣ㄥ涔犲钩鍙般€傜綉鍧€锛?ahref="http://www.scikit-learn.org"target="_blank"rel="nofollownoopener">www.scikit-learn.org銆?br/>

搴忓垪妯″瀷锛坰equencemodel锛?

杈撳叆鍏锋湁搴忓垪渚濊禆鎬х殑妯″瀷銆備緥濡傦紝鏍规嵁涔嬪墠瑙傜湅杩囩殑瑙嗛搴忓垪瀵逛笅涓€涓棰戣繘琛岄娴嬨€?br/>

Sigmoid鍑芥暟锛坰igmoidfunction锛?

softmax

涓哄绫诲埆鍒嗙被妯″瀷涓瘡涓彲鑳界殑绫绘彁渚涙鐜囩殑鍑芥暟銆傛鐜囧姞璧锋潵鐨勬€诲拰鏄?1.0銆備緥濡傦紝softmax鍙兘妫€娴嬪埌鏌愪釜鍥惧儚鏄竴鍙嫍鐨勬鐜囦负 0.9锛屾槸涓€鍙尗鐨勬鐜囦负 0.08锛屾槸涓€鍖归┈鐨勬鐜囦负 0.02銆傦紙涔熷彨浣?fullsoftmax锛夈€?br/>

缁撴瀯椋庨櫓鏈€灏忓寲锛坰tructuralriskminimization/SRM锛?

杩欑绠楁硶骞宠涓や釜鐩爣锛?br/>

鏋勫缓棰勬祴鎬ф渶寮虹殑妯″瀷锛堝鏈€浣庢崯澶憋級銆?br/>

浣挎ā鍨嬪敖閲忎繚鎸佺畝鍗曪紙濡傚己姝ｅ垯鍖栵級銆?br/>

姣斿锛屽湪璁粌闆嗕笂鐨勬崯澶辨渶灏忓寲+姝ｅ垯鍖栫殑妯″瀷鍑芥暟灏辨槸缁撴瀯椋庨櫓鏈€灏忓寲绠楁硶銆傛洿澶氫俊鎭紝鍙傝 http://www.svms.org/srm/銆傚彲涓庣粡楠岄闄╂渶灏忓寲瀵圭収闃呰銆?br/>

鐩戠潱寮忔満鍣ㄥ涔狅紙supervisedmachinelearning锛?

鍒╃敤杈撳叆鏁版嵁鍙婂叾瀵瑰簲鏍囩鏉ヨ缁冩ā鍨嬨€傜洃鐫ｅ紡鏈哄櫒瀛︿範绫讳技瀛︾敓閫氳繃鐮旂┒闂鍜屽搴旂瓟妗堣繘琛屽涔犮€傚湪鎺屾彙闂鍜岀瓟妗堜箣闂寸殑鏄犲皠涔嬪悗锛屽鐢熷氨鍙互鎻愪緵鍚屾牱涓婚鐨勬柊闂鐨勭瓟妗堜簡銆傚彲涓庨潪鐩戠潱鏈哄櫒瀛︿範瀵圭収闃呰銆?br/>

Similaritymeasure锛堢浉浼煎害搴﹂噺锛?

SingularValueDecomposition锛堝寮傚€煎垎瑙ｏ級

Softmargin锛堣蒋闂撮殧锛?

Softmarginmaximization锛堣蒋闂撮殧鏈€澶у寲锛?

SupportVectorMachine/SVM锛堟敮鎸佸悜閲忔満锛?

T

寮犻噺锛坱ensor锛?

TensorFlow椤圭洰鐨勪富瑕佹暟鎹粨鏋勩€傚紶閲忔槸 N缁存暟鎹粨鏋勶紙N鐨勫€煎緢澶э級锛岀粡甯告槸鏍囬噺銆佸悜閲忔垨鐭╅樀銆傚紶閲忓彲浠ュ寘鎷暣鏁般€佹诞鐐规垨瀛楃涓插€笺€?br/>

Transferlearning锛堣縼绉诲涔狅級

U

鏃犳爣绛炬牱鏈紙unlabeledexample锛?

鍖呭惈鐗瑰緛浣嗘病鏈夋爣绛剧殑鏍锋湰銆傛棤鏍囩鏍锋湰鏄帹鏂殑杈撳叆銆傚湪鍗婄洃鐫ｅ涔犲拰鏃犵洃鐫ｅ涔犵殑璁粌杩囩▼涓紝閫氬父浣跨敤鏃犳爣绛炬牱鏈€?br/>

鏃犵洃鐫ｆ満鍣ㄥ涔狅紙unsupervisedmachinelearning锛?

璁粌涓€涓ā鍨嬪鎵炬暟鎹泦锛堥€氬父鏄棤鏍囩鏁版嵁闆嗭級涓殑妯″紡銆傛棤鐩戠潱鏈哄櫒瀛︿範鏈€甯哥敤浜庡皢鏁版嵁鍒嗘垚鍑犵粍绫讳技鐨勬牱鏈€傛棤鐩戠潱鏈哄櫒瀛︿範鐨勫彟涓€涓緥瀛愭槸涓绘垚鍒嗗垎鏋愶紙principalcomponentanalysis锛孭CA锛?br/>

W

Wordembedding锛堣瘝宓屽叆锛?

Wordsensedisambiguation锛堣瘝涔夋秷姝э級

浅谈人工智能产品设计——情感分析

浜哄伐鏅鸿兘浜у搧鐨勫畾涔夎緝涓哄箍娉涳紝鏅鸿兘纭欢銆佹満鍣ㄤ汉銆佽姱鐗囥€佽闊冲姪鎵嬬瓑閮藉彲浠ュ彨鍋氫汉宸ユ櫤鑳戒骇鍝併€傛湰鏂囪璁虹殑浜哄伐鏅鸿兘浜у搧涓昏鏄寚鍦ㄤ簰鑱旂綉浜у搧涓繍鐢ㄤ汉宸ユ櫤鑳芥妧鏈€?br/>

浜掕仈缃戜骇鍝佷富瑕佺潃鎵嬩笌瑙ｅ喅鐢ㄦ埛鐨勭棝鐐癸紝瀵逛簬C绔骇鍝佹潵璇达紝鐥涚偣灏辨槸鎸囩殑涓汉鎯宠В鍐宠€屾棤娉曡В鍐崇殑闂锛屽涓汉鎯宠缇庡寲鑷繁鐨勭収鐗囷紝浣嗘槸浠栦笉浼氬鏉傜殑PS杞欢锛屼簬鏄編鍥剧绉€灏卞彲浠ヨВ鍐宠繖涓棝鐐广€備粠KANO妯″瀷涓紝灏辨槸婊¤冻鐢ㄦ埛鐨勫熀鏈渶姹備笌鏈熸湜闇€姹傘€?br/>

浜哄伐鏅鸿兘浜у搧锛堝湪浜掕仈缃戜骇鍝佷腑杩愮敤浜哄伐鏅鸿兘鎶€鏈級鍒欐槸瑕佹弧瓒崇敤鎴风殑鍏村闇€姹傘€傚灏嗘儏鎰熷垎鏋愯繍鐢ㄥ埌鐢靛晢鐨勪骇鍝佽瘎璁轰腑锛岀敤鎴峰垯鍙互閫氳繃鍙鍖栫殑鏁版嵁灞曠ず鏉ュぇ鑷村浜у搧鏈変釜鍏ㄩ潰銆佺洿瑙傜殑浜嗚В锛岃€屼笉鍐嶉渶瑕佽嚜宸变竴椤典竴椤电殑缈荤湅璇勮鍐呭銆?br/>

浜掕仈缃戜骇鍝佷富瑕佸叧娉ㄧ偣鍦ㄤ簬鐢ㄦ埛闇€姹傘€佹祦绋嬭璁°€佷氦浜掕璁°€佸晢涓氭ā寮忕瓑銆傜潃鐪间簬鐢ㄦ埛闇€姹傦紝璁捐婊¤冻鐢ㄦ埛闇€姹傜殑浜у搧锛岄€氳繃鍚堢悊鐨勬祦绋嬭璁°€佷氦浜掕璁¤揪鍒颁骇鍝佺洰鏍囷紝杩涜€屽疄鐜板晢涓氱洰鏍囥€傚吀鍨嬬殑鎬濊矾鏄彂鐜扮敤鎴烽渶姹傗€斺€?gt;璁捐婊¤冻鐢ㄦ埛闇€姹傜殑浜у搧鈥斺€?gt;杩唬瀹屽杽銆佷骇鍝佽繍钀モ€斺€?gt;鍟嗕笟鍙樼幇銆?br/>

浜哄伐鏅鸿兘浜у搧鍏虫敞鐐瑰湪浜庢ā鍨嬬殑鏋勫缓锛屽畠涓嶅啀鏄浜庡竷灞€銆佷氦浜掔殑鎺ㄦ暡锛岃€屾槸閫氳繃閫夋嫨鍚堥€傜殑鏁版嵁锛屾瀯寤哄悎閫傜殑妯″瀷锛屾渶缁堝憟鐜板嚭鏉ョ殑鏄ソ鐨勬晥鏋溿€備粈涔堟槸濂界殑鏁堟灉鍛紵杩欏氨闇€瑕佸紩鍏ヨ瘎娴嬫寚鏍囥€備簰鑱旂綉鐨勮瘎娴嬫寚鏍囨湁鎴戜滑鐔熺煡鐨勭暀瀛樼巼銆佽浆鍖栫巼銆佹棩娲昏穬绛夛紝閭ｄ箞浜哄伐鏅鸿兘鐨勪骇鍝佷富瑕佹槸閫氳繃涓€浜涚粺璁℃寚鏍囨潵鎻忚堪锛屼互鎯呮劅鍒嗘瀽涓轰緥锛屾妸鎯呮劅鍒嗘瀽鐪嬫垚涓€涓垎绫婚棶棰橈紝鍒欏彲浠ヤ娇鐢≒銆丷銆丄銆丗鍊兼潵鎻忚堪銆?br/>

1锛夋煡鍑嗙巼锛圥recision锛夛細P鍊硷紝琛￠噺鏌愮被鍒嗙被涓瘑鍒纭殑姣斾緥锛屽鎯呮劅鍒嗘瀽涓紝鏈?0鏉¤鍒嗙被涓衡€滄鍚戔€濓紝鍏朵腑8鏉℃槸鍒嗙被姝ｇ‘鐨勶紙鐢变汉宸ュ鏍革級锛岄偅涔圥=8/10=80%

2锛夋煡鍏ㄧ巼锛圧ecall锛夛細R鍊硷紝鍙堝彨鏌ュ叏鐜囷紝鍙堝彨鍙洖搴︼紝鎸囩殑鏄煇绫昏琚纭垎绫荤殑姣斾緥锛屽悓鏍蜂互鎯呮劅鍒嗘瀽涓轰緥锛?00鏉℃暟鎹腑鏈?0鏉℃槸姝ｅ悜鐨勶紝鏈哄櫒鍒嗙被鍚庯紝杩?0鏉′腑鏈?鏉¤鍒嗙被涓烘鍚戯紝鍒橰=7/10=70%.

3)F鍊硷紝鍥犱负P鍊煎拰R鍊奸€氬父鏄袱涓浉浜掔煕鐩剧殑鎸囨爣锛屽嵆涓€涓秺楂樺彟涓€涓秺浣庯紝F鍒欐槸涓よ€呯患鍚堣€冭檻鐨勬寚鏍囷紝涓嶈€冭檻璋冭妭P銆丷鏉冮噸鐨勬儏鍐典笅锛孎=2PR/(P+R)

4锛夌簿纭害锛圓ccuracy锛夛細杩欎釜鏈€濂界悊瑙ｏ紝灏辨槸琚噯纭垎绫荤殑姣斾緥锛屼篃灏辨槸姝ｇ‘鐜囥€傚100鏉℃暟鎹紝90鏉℃槸琚纭垎绫荤殑锛屽垯A=90/100=90%銆?br/>

浠ヤ笂鎸囨爣瓒婇珮锛岃鏄庢ā鍨嬫晥鏋滆秺濂姐€?br/>

鎴戜滑浠庝笂闈㈠唴瀹瑰彲浠ョ煡閬擄紝浜哄伐鏅鸿兘浜у搧璁捐鍏虫敞锛氭暟鎹€斺€?gt;妯″瀷鈥斺€?gt;鏁堟灉璇勪及銆?br/>

鐜板湪鎴戜滑浠ユ儏鎰熷垎鏋愪负渚嬪瓙璇存槑浜у搧璁捐鐨勮繃绋嬨€?br/>

1锛夋暟鎹細

鏁版嵁鐨勯€夋嫨瀵规渶缁堟ā鍨嬬殑缁撴灉鏈夌洿鎺ュ奖鍝嶏紝鎯呮劅鍒嗘瀽锛屾牴鎹笉鍚岀殑鐩殑锛岄€夋嫨鐨勬暟鎹篃涓嶅悓銆傚灏嗘儏鎰熷垎鏋愯繍鐢ㄤ簬鐢靛奖绁ㄦ埧棰勬祴锛屽垯涓€浜涙洿鏂板強鏃躲€佸唴瀹逛赴瀵岀殑鏁版嵁婧愶紝濡傚井鍗氾紝鏄瘮杈冨ソ鐨勯€夋嫨銆傚鏋滄槸搴旂敤浜庡晢鍝佺殑璇勪环锛屽鐢靛瓙浜у搧锛屽緢澶氳瘎娴嬪唴瀹规槸鏃犳硶鍦ㄧ煭鐭嚑鍙ヨ瘽鍐呮弿杩版竻妤氱殑锛岃繖鏃跺€欏井鍗氫笉鏄釜濂界殑閫夋嫨锛岄€夋嫨璁哄潧涓婃洿鏂拌緝鎱€佷絾鏄缁嗙殑鍐呭灏辨瘮杈冮€傚悎銆?br/>

濡傛灉鑳藉湪浜у搧鐨勬棭鏈熷氨鏈夊紩鍏ヤ汉宸ユ櫤鑳界殑鎵撶畻锛屽垯鍙互鍦ㄤ骇鍝佷腑浜嬪厛鍋氬ソ鏁版嵁閲囬泦銆?br/>

2锛夋ā鍨嬶細

鍦ㄩ€夋嫨妯″瀷涓紝浜у搧闇€瑕佷簡瑙ｄ笉鍚岀殑妯″瀷鐨勪紭缂虹偣锛岃繘鑰岄€夋嫨鏇村姞鍚堥€傜殑妯″瀷銆傚湪鎯呮劅鍒嗘瀽涓紝NB銆丼VM銆丯-gram閮芥槸甯哥敤鐨勬ā鍨嬶紝鍏朵腑SVM鏁堟灉鏈€濂斤紙杩欐槸宸叉湁鐨勭粨璁猴級锛屽鏋滄槸鍏朵粬鐨勬櫤鑳戒骇鍝侊紝鍙兘闇€瑕佺畻娉曞洟闃熻繘琛屽疄楠岋紝缁欏嚭娴嬭瘯鏁版嵁锛岃繘鑰岄€夋嫨鍚堥€傜殑妯″瀷銆?br/>

3锛夋晥鏋滆瘎浼帮細

鏁堟灉璇勪及鍦ㄤ笂鏂囦腑宸茬粡鎻忚堪寰楁瘮杈冩竻妤氾紝鍏蜂綋鎸囨爣涓嶅啀璧樿堪銆?br/>

4锛変骇鍝佸憟鐜帮細

鏈€鍚庤繖涓€姝ワ紝鏄皢缁撴灉灞曠ず缁欑敤鎴枫€傚湪鎯呮劅鍒嗘瀽涓紝鎴戜滑鍙互閫夋嫨闆疯揪鍥俱€佽瘝浜戙€佹儏鎰熻秼鍔垮浘鏉ュ睍绀虹粨鏋溿€傚彇鍐充簬浜у搧灞炴€э紝濡傜數鍟嗕骇鍝佽瘎璁烘寲鎺橈紝鍙互浣跨敤璇嶄簯锛?br/>

濡傝垎璁哄垎鏋愶紝鍙互浣跨敤鎯呮劅瓒嬪娍鍥俱€?br/>

浜哄伐鏅鸿兘浜у搧鐨勮璁¤鍏虫敞锛氭暟鎹€佹ā鍨嬨€佽瘎鍒ゃ€佸憟鐜般€?

计算神经科学能否成为未来人工智能的发展方向？

基本概念

人工智能：AI(ArtificialIntelligence)是研究使计算机来模拟人的某些思维过程和智能行为（如学习、推理、思考、规划等）的学科，主要包括计算机实现智能的原理、制造类似于人脑智能的计算机，使计算机能实现更高层次的应用。人工智能是计算机科学、心理学、哲学和语言学等多学科的交叉，其范围已远远超出了计算机科学的范畴，从思维观点看，人工智能不仅限于逻辑思维，要考虑形象思维、灵感思维才能促进人工智能的突破性的发展。

神经科学：神经科学指寻求解释精神活动的生物学机制，神经科学寻求在个体生长发育过程中的神经回路感知世界、反应生成、行为实现，以及从记忆中寻找曾经感知过的知觉、探寻的知觉对个体的影响等机理，其复杂程度远超过任何人们在其他生物学领域中曾经面对的问题。

AI与神经科学的研究核心

人工智能是研究开发能够模拟、延伸和扩展人类智能的理论、方法、技术及应用系统的一门新的技术科学，研究对象是智能操控，现阶段研究方法上是侧重于对复杂现象进行模拟仿真的“计算科学”。

神经科学更多地侧重于生物学意义上的神经活动的规律，解析包括思维、情感、智能等在内的高级神经活动的发生机制，而意识起源问题，则是神经科学的终极目标，研究方法上神经科学是以自然现象归纳为主的“实验科学”。

AI与神经科学的联系

对于神经科学与人工智能的关系，我们可以以一个闭环的水源和水流的概念去理解。人工智能的兴起，一方面源于科学技术的发展，另一方面则受神经科学领域成果的影响，在两者的关系之中，人工智能兴起于神经科学，并且人工智能的发展促进了神经科学领域的研究，而神经科学领域的进步又促进了人工智能的发展，在目前的技术层面下，两者形成一个闭环的发展关系，相互滋养、相互发展进步。

伴随着脑与神经科学、认知科学的进展使得人们在脑区、神经微环路、神经元等不同尺度观测的各种认知任务中，获取脑组织的部分活动数据已经实现，获知人脑信息处理过程也有了数据依据，并且多学科交叉的实验研究得出的人脑工作机制更具可靠性。因此，脑科学的发展，海量实验数据的有力支撑，有望为机器学习、类脑计算的突破提供现实依据以参考借鉴。

在神经科学基础研究阶段，人工智能可以辅助研究人员解析复杂的脑神经信号、脑神经图谱实验数据，构建和模拟大脑模型系统等。在转化应用阶段，人工智能还能加速脑科学成果的应用，例如大脑疾病诊断与新疗法成果的临床转化等。人工智能对神经科学发展的反哺或反馈作用也是客观存在的。

元学习——AI与神经科学的紧密结合

机器学习的主要优势在于能够识别复杂数据中的模式，尤其在涉及到分析人类的思想时。大脑发出的信号真的很复杂。随着机器学习的推进，神经科学家正在破解数十亿个大脑神经元协同工作的秘密。例如：功能性磁共振成像通过检测血液流动的变化来测量大脑的活动，它每秒都能生成大脑活动的高维快照。使用机器学习来分析数据有助于发现大脑活动的方式，从而加快研究工作。

在机器学习里，我们会使用某个场景的大量数据来训练模型，比如：训练一个可以识别锦鲤图像的模型，我们需要大量的关于锦鲤的数据集，通过特定的算法程序实现对锦鲤图像的识别，然而一旦当场景发生改变，比如拿着一个可以识别锦鲤模型想要去识别海豹，模型就需要根据新的数据集重新训练。因此，元学习的概念应运而生。

元学习(MetaLearning)，具体指的是learntolearn，MetaLearning希望使模型获取一种“学会学习”的能力。使其可以在获取已有“知识”的基础上快速学习新的任务。如：

让Alphago(下围棋的)迅速学会下象棋让一个向日葵图片分类器，迅速具有分类其他物体的能力

在机器学习中，训练单位是一条条数据，通过数据来对模型进行优化；

在元学习中，训练单位分两个层级，第一层训练单位是一种学习方法，元学习中要准备许多学习方法来进行学习，第二层的训练单位才是对应的一条数据。

现在AI的发展在数据层面的训练模型已经发展到了一定的高度，而学习方法层面训练模型构造——元学习技术突破的难点，就和人类的神经科学发展密切相关，人类同一类型下的事物就会比较容易上手，比如：你会JAVA编程，掌握了编程的基本思想，熟悉了面向对象的基本概念，那么上手Python将会比纯新手入门Python要容易得多，但现在的深度学习模型在遇到类似问题的时候，即使是很类似的情况也需要从0开始重新学习！这一人类智能和AI的差异就导致了meta-learning的产生。

现在的元学习大致可以分为以下4类：

基于优化的：其中最火的就是MAML，还有之前的Meta-LSTM等等。基于度量的：包括原型网络，孪生网络，匹配网络，关系网络。基于模型的：利用RNN网络和外部存储来实现“记忆”基于GNN

即便元学习现在还处于发展初期，但机器学习对于神经科学领域的发展却已经逐步进入我们的生活。机器学习的主要优势在于能够识别复杂数据中的模式，尤其在涉及到分析人类的思想时，大脑发出的信号十分抽象，所需要采集的数据数量十分庞大，并且之间的关系十分复杂。随着机器学习的推进，神经科学家正在破解数十亿个大脑神经元协同工作的秘密。例如：功能性磁共振成像通过检测血液流动的变化来测量大脑的活动，它每秒都能生成大脑活动的高维快照。使用机器学习来分析数据有助于发现大脑活动的方式，从而加快研究工作。

前景

人工智能与神经科学的发展可能会经过以下三个阶段：

第一个阶段是在人工智能初期发展的影响下，解决一些神经科学基础实验数据的处理，进而加快神经科学领域发展进程，在这一阶段人工智能和大数据技术是神经科学发展的“加速器”年，等到神经科学将迎来第一轮重大突破，在神经感知和神经认知理解方面出现突破性成果时，必然反哺、革新原有人工智能的算法基础和元器件基础，进而人类社会进入实质性类脑智能研究阶段。第二个阶段等待神经科学迎来第二轮重大突破之时在情感、意识理解方面出现颠覆性成果，开发出一个多尺度、整合、可验证的大脑模型理论，类脑智能进入全新阶段，并将推动人脑的超生物进化，神经科学和类脑智能学科融为一体，人类社会全面进入强人工智能时代。第三个阶段，随着技术的不断成熟，围绕神经科学和人工智能，特别是强人工智能，开始会衍生出许多科学理论和社会与伦理方面的问题。

人工智能目前存在的问题源于对神经科学的了解程度有限，限制了设计中并无法充分考虑真实的大脑情况。但如果通过对人脑的逆向工程来揭示大脑的秘密，或许就能更好地设计出能同时处理多重信息流的计算设备，在高新技术计算模型的更新迭代之下，在神经科学领域的数据采集、数据处理下，在极大程度上能很好地帮助研究人员快速、有效、全面地掌握人脑神经的规律，进而颠覆性加速神经科学领域的发展，并且这个发展速度，是随着底层技术的不断提升而提升的。

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