Thispracticalguideprovidesnearly200self-containedrecipestohelpyousolvemachinelearningchallengesyoumayencounterinyourdailywork.Ifyou’recomfortablewithPythonanditslibraries,includingpandasandscikit-learn,you’llbeabletoaddressspecificproblemssuchasloadingdata,handlingtextornumericaldata,modelselection,anddimensionalityreductionandmanyothertopics.Eachrecipeincludescodethatyoucancopyandpasteintoatoydatasettoensurethatitactuallyworks.Fromthere,youcaninsert,combine,oradaptthecodetohelpconstructyourapplication.Recipesalsoincludeadiscussionthatexplainsthesolutionandprovidesmeaningfulcontext.Thiscookbooktakesyoubeyondtheoryandconceptsbyprovidingthenutsandboltsyouneedtoconstructworkingmachinelearningapplications.You’llfindrecipesfor:Vectors,matrices,andarraysHandlingnumericalandcategoricaldata,text,images,anddatesandtimesDimensionalityreductionusingfeatureextractionorfeatureselectionModelevaluationandselectionLinearandlogicalregression,treesandforests,andk-nearestneighborsSupportvectormachines(SVM),naïveBayes,clustering,andneuralnetworksSavingandloadingtrainedmodels

在复现ACL2020的论文ANovelCascadeBinaryTaggingFrameworkforRelationalTripleExtraction中需要的raw_NYT数据，由于googledrive不太好下载所以放这了，并且贴心的把7z压缩格式变成了常见的zip，我可真是个小天使

SLAM新手入门史上最详细介绍。
SLAMforDummies-ATutorialApproachtoSimultaneousLocalizationandMappingBythe‘dummies’SørenRiisgaardofcontents1.TABLEOFCONTENTS.........................................................................................................22.INTRODUCTION...................................................................................................................43.ABOUTSLAM........................................................................................................................64.THEHARDWARE..................................................................................................................7THEROBOT....................................................................................................................................7THERANGEMEASUREMENTDEVICE.................................................................................................85.THESLAMPROCESS.........................................................................................................106.LASERDATA.......................................................................................................................147.ODOMETRYDATA.............................................................................................................158.LANDMARKS......................................................................................................................169.LANDMARKEXTRACTION..............................................................................................19SPIKELANDMARKS.......................................................................................................................19RANSAC....................................................................................................................................20MULTIPLESTRATEGIES..................................................................................................................2410.D

codeforLatentLow-RankRepresentationforSubspaceSegmentationandFeatureExtractioniccv2011

文档包括两个分别是OpenSmile官方英文文档，另外一个是语音特征提取的原理及计算方法。
文档一名称：openSMILE-book-latest，文档二名称：Real-timeSpeechandMusicClassificationbyLargeAudioFeatureSpaceExtraction

Graph-basedreasoningmodelformultiplerelationextraction.pdf

Manyresearchgroupsinacademiaandindustryarefocusingontheperformanceimprovementofelectronicnose(E-nose)systemsmainlyinvolvingthree.optimizations,whicharesensitivematerialselectionandsensorarrayoptimization,enhancedfeatureextractionmethodsandpatternrecognitionmethodselection.Foraspecificapplication,thefeatureextractionmethodisabasicpartofthesethreeoptimizationsandakeypointinE-nosesystemperformanceimprovement.Theaimofafeatureextract

Inspiredbythefactthatedgeisanimportantcuetodistinguishtextsfrombackground,weproposeanovelscenetextdetectionmethodviaedgecueandmultiplefeatures,whichhastwomainparts,i.e.candidatecharacterregion(CCR)extractionandregionclassification.ForCCRextraction,theed

资源包含iTextSharp7(net40及netstandard1.6下的库文件)，iText.kernel源码和读取表格数据源码，运行TableExtractionFromPDF项目可查看效果。
iText.kernel版本7.1.3.0iText.io版本7.1.3.0原文网址：https://www.codeproject.com/Tips/1262815/Extract-Tables-from-PDFs

Notalllanguages,e.g.Chinese,havedelimitersforwords.Toextractwordsfromasentenceintheselanguages,weusuallyrelyonadictionaryforknownwords.Forunknownwords,someapproachesrelyonadomainspecificdictionaryoratailor-madelearningdataset.However,thisinformationmaynotbeavailable.Anotherdirectionistouseunsupervisedmethods.Thesemethodsrelyonagoodnessmeasuretoevaluatehowlikelythewordsaremeaningfulbasedonastatisticalargumentonthegive

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。