Firstrelease:26October2017www.sciencemag.org(Pagenumbersnotfinalattimeoffirstrelease)1Theabilitytolearnandgeneralizefromafewexamplesisahallmarkofhumanintelligence(1).CAPTCHAs,imagesusedbywebsitestoblockautomatedinteractions,areexamplesofproblemsthatareeasyforhumansbutdifficultforcomput-ers.CAPTCHAsarehardforalgorithmsbecausetheyaddclutterandcrowdletterstogethertocreateachicken-and-eggproblemforcharacterclassifiers—theclassifiersworkwellforcharactersthathavebeensegmentedout,butsegmentingtheindividualcharactersrequiresanunderstandingofthecharacters,eachofwhichmightberenderedinacombinato-rialnumberofways(2–5).Arecentdeep-learningapproachforparsingonespecificCAPTCHAstylerequiredmillionsoflabeledexamplesfromit(6),andearlierapproachesmostlyreliedonhand-craftedstyle-specificheuristicstosegmentoutthecharacter(3,7);whereashumanscansolvenewstyleswithoutexplicittraining(Fig.1A).Thewidevarietyofwaysinwhichletterformscouldberenderedandstillbeunder-stoodbypeopleisillustratedinFig.1.Buildingmodelsthatgeneralizewellbeyondtheirtrain-ingdistributionisanimportantsteptowardtheflexibilityDouglasHofstadterenvisionedwhenhesaidthat“foranyprogramtohandleletterformswiththeflexibilitythathumanbeingsdo,itwouldhavetopossessfull-scaleartificialintelli-gence”(8).Manyresearchershaveconjecturedthatthiscouldbeachievedbyincorporatingtheinductivebiasesofthevis-ualcortex(9–12),utilizingthewealthofdatageneratedbyneuroscienceandcognitivescienceresearch.Inthema妹妹a-lianbrain,feedbackconnectionsinthevisualcortexplayrolesinfigure-ground-segmentation,andinobject-basedtop-downattentionthatisolatesthecontoursofanobjectevenwhenpartiallytransparentobjectsoccupythesamespatiallocations(13–16).Lateralconnectionsinthevisualco

基于neurosky的脑电采集套件，vs2010上数据接纳DEMO

Neuro-DynamicProgra妹妹ingbyDimitriP.BertsekasandJohnTsitsiklis

Informationtheoryandinference,taughttogetherinthisexcitingtextbook,lieattheheartofmanyimportantareasofmoderntechnology-co妹妹unication,signalprocessing,datamining,machinelearning,patternrecognition,computationalneuroscience,bioinformaticsandcryptography.Thebookintroducestheoryintandemwithapplications.Informationtheoryistaughtalongsidepracticalco妹妹unicationsystemssuchasarithmeticcodingfordatacompressionandsparse-graphcodesforerror-correction.Inferencetechniques,includingmessage-passingalgorithms,MonteCarlomethodsandvariationalapproximations,aredevelopedalongsideapplicationstoclustering,convolutionalcodes,independentcomponentanalysis,andneuralnetworks.Uniquely,thebookcoversstate-of-the-arterror-correctingcodes,includinglow-density-parity-checkcodes,turbocodes,anddigitalfountaincodes-thetwenty-first-centurystandardsforsatelliteco妹妹unications,diskdrives,anddatabroadcast.Richlyillustrated,filledwithworkedexamplesandover400exercises,somewithdetailedsolutions,thebookisidealforself-learning,andforundergraduateorgraduatecourses.Italsoprovidesanunparalleledentrypointforprofessionalsinareasasdiverseascomputationalbiology,financialengineeringandmachinelearning.

PolycomRealPresence桌面是一个易于使用的视频协作使用程序,为PC用户提供高清质量的音频,视频和内容共享。
与类似的专有使用程序不同,RealPresence桌面是基于标准的,并扩展了组织现有的视频启用网络,使最终用户能够在办公室内或在路上使用相同的高质量体验。
ENDUSERLICENSEAGREEMENTFORPOLYCOM®SOFTWAREIMPORTANT-READCAREFULLYBEFOREUSINGTHESOFTWARE:ThisEnd-UserLicenseAgreement("Agreement")isalegalagreementbetweenyouand/oranycompanyyourepresent,“you”andeitherPolycom(Netherlands)B.V.(ifyouarelocatedinEurope,MiddleEast,orAfrica),PolycomAsiaPacificPTELtd.(ifyouarelocatedinAsiaPacific),orPolycom,Inc.(ifyouarelocatedintherestoftheworld)(eachreferredtoindividuallyandcollectivelyhereinas“POLYCOM”),fortheSOFTWAREPRODUCT(asdefinedbelow)licensedbyPOLYCOMoritssuppliers.UnlessotherwiseagreedinwritingbyPOLYCOM,byacceptingthesetermsorbyinstalling,downloading,copying,orotherwiseusingtheSOFTWAREPRODUCT,youagreetobeandwillbeboundbythetermsofthisAgreementasaconditionofyourlicenseandthetermsandconditionsofthisAgreementwillprevailoveranyinconsistent,conflicting,additionalorpreprintedterms.IfyoudonotagreetothetermsofthisAgreement,youruseisprohibitedandyoumaynotinstallorusetheSOFTWAREPRODUCT.TheSOFTWAREPRODUCTisprotectedbycopyrightlawsandinternationalcopyrighttreaties,aswellasotherintellectualpropertylawsandtreaties.TheSOFTWAREPRODUCTislicensed(notsold)toyou,anditsuseissubjecttothetermsofthisAgreement.ThisisNOTasalecontract.DEFINITIONS“SOFTWAREPRODUCT”meanscomputersoftware,includinganysoftwareupdatesorupgradesthereto,andassociatedmedia,printedmaterials,and"online"orelectronicdocumentationidentifiedonanAUTHORIZEDPURCHASEORDER.“CONCURRENTUSER”isatypeoflicenseandmeansthenumberofpersonaldevices,endpoints,applicationsoranyotherauthorizedmeansofaccessthatmayconcurrentlyuseoraccesstheSOFTWAREPRODUCToron

可以完成神经网络方面的很多基本成绩,是专门做预测成绩的.

iNeuron-AS

（含源码及报告）本程序分析了自2016年到2021年（外加）每年我国原油加工的产量，并且分析了2020年全国各地区原油加工量等，含饼状图，柱状图，折线图，数据在地图上显示。
运转本程序需要requests、bs4、csv、pandas、matplotlib、pyecharts库的支持，如果缺少某库请自行安装后再运转。
文件含6个excel表，若干个csv文件以及一个名字为render的html文件（需要用浏览器打开），直观的数据处理部分是图片以及html文件，可在地图中显示，数据处理的是excel文件。
不懂可以扫文件中二维码在QQ里面问。

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。