《Science》杂志-机器学习究竟将如何影响人类未来的工作-中文版+英文原版，帮助大家对于机器学习最新的发展趋势进行了解

RodriguezA,LaioA.Clusteringbyfastsearchandfindofdensitypeaks[J].Science,2014,344(6191):1492-1496.基于这篇文章实现的最基本的密度聚类的算法密度峰值聚类py代码

等距映射的源代码，使用MATLAB编写，具体方法可见2000年Science文献

Writing_Science_How_to_Write_Papers_That_Get_Cited_and_Proposals_That_Get_Funded

NUMERICALOPTIMIZATIONpresentsacomprehensiveandup-to-datedescriptionofthemosteffectivemethodsincontinuousoptimization.Itrespondstothegrowinginterestinoptimizationinengineering,science,andbusinessbyfocusingonthemethodsthatarebestsuitedtopracticalproblems.Drawingontheirexperiencesinteaching,research,andconsulting,theauthorshaveproducedatextbookthatwillbeofinteresttostudentsandpractitionersalike.Eachchapterbeginswiththebasicconceptsandbuildsupgraduallytothebesttechniquescurrentlyavailable.Becauseoftheemphasisonpracticalmethods,aswellastheextensiveillustrationsandexercises,thebookisaccessibletoawideaudience.Itcanbeusedasagraduatetextinengineering,operationsresearch,mathematics,computerscience,andbusiness.Italsoservesasahandbookforresearchersandpractitionersinthearea.Aboveall,theauthorshavestrivedtoproduceatextthatispleasanttoread,informativeandrigorous--onethatrevealsboththebeautifulnatureofthedisciplineanditspracticalside.

Jordan和Mitchell两位Machinelearning大牛在Science杂志综述长文——机器学习发展趋势和前景

RodriguezA,LaioA.Clusteringbyfastsearchandfindofdensitypeaks[J].Science,2014,344(6191):1492-1496.基于这篇文章实现的最基本的密度聚类的算法，具体请看我博客中的相关文章http://blog.csdn.net/kryolith/article/details/39832573

这是一篇来自science杂志的论文，极其典型！介绍了测地距离在流行降维中的使用。
Scientistsworkingwithlargevolumesofhigh-dimensionaldata,suchasglobalclimatepatterns,stellarspectra,orhumangenedistributions,regularlyconfronttheproblemofdimensionalityreduction:Þndingmeaningfullow-dimensionalstructureshiddenintheirhigh-dimensionalobservations.Thehumanbrainconfrontsthesameproblemineverydayperception,extractingfromitshigh-dimensionalsensoryinputsÑ30,000auditorynerveÞbersor106opticnerveÞbersÑamanageablysmallnumberofperceptuallyrelevantfeatures.Herewedescribeanapproachtosolvingdimensionalityreductionproblemsthatuseseasilymeasuredlocalmetricinformationtolearntheunderlyingglobalgeometryofadataset.Unlikeclassicaltechniquessuchasprincipalcomponentanalysis(PCA)andmultidimensionalscaling(MDS),ourapproachiscapableofdiscoveringthenonlineardegreesoffreedomthatunderliecomplexnaturalobservations,suchashumanhandwritingorimagesofafaceunderdifferentviewingconditions.Incontrasttopreviousalgorithmsfornonlineardimensionalityreduction,oursefÞcientlycomputesagloballyoptimalsolution,and,foranimportantclassofdatamanifolds,isguaranteedtoconvergeasymptoticallytothetruestructure.

文章中文翻译，整理成文档方便看。
出处:http://blog.csdn.net/itplus/article/details/38926837接待转载/分享,但请务必声明文章出处.

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

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。