TheJupyterNotebookallowsyoutocreateandsharedocumentsthatcontainlivecode,equations,visualizations,andexplanatorytext.TheJupyterNotebooksystemisextensivelyusedindomainssuchasdatacleaningandtransformation,numericalsimulation,statisticalmodeling,andmachinelearning.LearningJupyter5willhelpyougettogripswithinteractivecomputingusingreal-worldexamples.ThebookstartswithadetailedoverviewoftheJupyterNotebooksystemanditsinstallationindifferentenvironments.Next,youwilllearntointegratetheJupytersystemwithdifferentprogramminglanguagessuchasR,Python,Java,JavaScript,andJulia,andexplorevariousversionsandpackagesthatarecompatiblewiththeNotebooksystem.Movingahead,youwillmasterinteractivewidgetsandnamespacesandworkwithJupyterinamulti-usermode.Bytheendofthisbook,youwillhaveusedJupyterwithabigdatasetandbeabletoapplyallthefunctionalitiesyou’veexploredthroughoutthebook.YouwillalsohavelearnedallabouttheJupyterNotebookandbeabletostartperformingdatatransformation,numericalsimulation,anddatavisualization.

数据集为17CategoryFlowerDataset，是牛津大学VisualGeometryGroup选取的在英国比较常见的17种花；
其中每种花有80张图片，整个数据集有1360张图片；
类别已经分好，标签就是最外层的文件夹的名字，在输入标签的时候可以直接通过文件读取的方式。

MicrosoftExcel2019PivotTableDataCrunching(BusinessSkills)By作者:BillJelen–MichaelAlexanderISBN-10书号:1509307249ISBN-13书号:9781509307241Edition版本:1出版日期:2019-01-18pages页数:(512)RenownedExcelexpertsBillJelen(MrExcel)andMichaelAlexanderhelpyoucrunchdatafromanysourcewithExcel2019pivottables.UseExcel2019pivottablesandpivotchartstoproducepowerful,dynamicreportsinminutesinsteadofhours,totakecontrolofyourdataandyourbusiness.Evenifyou’venevercreatedapivottablebefore,thisbookwillhelpyouleveragealltheirremarkableflexibilityandanalyticalpower–includingvaluableimprovementsinExcel2019andExcelinOffice365.Drawingonmorethan45combinedyearsofExcelexperience,BillJelenandMichaelAlexanderofferpractical“recipes”forsolvingrealbusinessproblems,helpyouavoidcommonmistakes,andpresenttipsandtricksyou’llfindnowhereelse.Byreadingthisbook,youwill:Mastereasy,powerfulwaystocreate,customize,change,andcontrolpivottablesControlallfuturepivottablesusingnewpivottabledefaultsTransformhugedatasetsintoclearsummaryreportsInstantlyhighlightyourmostprofitablecustomers,products,orregionsUsePowerQuerytoquicklyimport,clean,shape,andanalyzedisparatedatasourcesBuildgeographicalpivottableswith3DMapConstructandsharestate-of-the-artdynamicdashboardsRevampanalysesontheflybydragginganddroppingfieldsBuilddynamicself-servicereportingsystemsShareyourpivottableswithcolleaguesCreatedatamashupsusingthefullPowerPivotcapabilitiesofExcel2019andExcelinOffice365AutomatepivottableswithmacrosandVBASavetimebyadaptingreportswithGetPivotDataDiscovertoday’smostusefulpivottabletipsandshortcuts

WikiText英语词库数据WikiText英语词库数据

包含作业jupyter文件，内有答案，lr_utils工具及数据集等

TransferringDeepConvolutionalNeuralNetworksfortheSceneClassificationofHigh-ResolutionRemo所用数据源WHU-RSDataset.从GoogleEarth（GoogleInc.）收集的WHU-RS数据集[6]是一个新的公开可用的数据集，其包含大小为600×600像素的950幅图像，均匀分布在19个场景类中。
一些示例图像如图5所示。
我们可以看到，一些类别中的照明，尺度，分辨率和viewpoint-dependent外观的变化使得它比UCM数据集更复杂。

某课网课程中的python实现数据诊断工具，整合了获取数据各项指标的方法，添加了注释，有些未涉及的指标照着代码添加方法即可

Formanycomputervisionproblems,themosttimeconsumingcomponentconsistsofnearestneighbormatchinginhigh-dimensionalspaces.Therearenoknownexactalgorithmsforsolvingthesehigh-dimensionalproblemsthatarefasterthanlinearsearch.Approximatealgorithmsareknowntoprovidelargespeedupswithonlyminorlossinaccuracy,butmanysuchalgorithmshavebeenpublishedwithonlyminimalguidanceonselectinganalgorithmanditsparametersforanygivenproblem.Inthispaper,wedescribeasystemthatanswersthequestion,“Whatisthefastestapproximatenearest-neighboralgorithmformydata?”Oursystemwilltakeanygivendatasetanddesireddegreeofprecisionandusethesetoautomaticallydeterminethebestalgorithmandparametervalues.Wealsodescribeanewalgorithmthatappliesprioritysearchonhierarchicalk-meanstrees,whichwehavefoundtoprovidethebestknownperformanceonmanydatasets.Aftertestingarangeofalternatives,wehavefoundthatmultiplerandomizedk-dtreesprovidethebestperformanceforotherdatasets.Wearereleasingpublicdomaincodethatimplementstheseapproaches.Thislibraryprovidesaboutoneorderofmagnitudeimprovementinquerytimeoverthebestpreviouslyavailablesoftwareandprovidesfullyautomatedparameterselection.

TUM的一个SLAM数据集，太大了所以拆成了三部分，每部分都是1积分，在国内下载实在是太慢了，我下了两天才下好。
数据集是https://vision.in.tum.de/data/datasets/rgbd-dataset/download的fr1/xyz

AuthorAnkurPatelprovidespracticalknowledgeonhowtoapplyunsupervisedlearningusingtwosimple,production-readyPythonframeworks-scikit-learnandTensorFlowusingKeras.Withthehands-onexamplesandcodeprovided,youwillidentifydifficult-to-findpatternsindataandgaindeeperbusinessinsight,detectanomalies,performautomaticfeatureengineeringandselection,andgeneratesyntheticdatasets.Allyouneedisprogrammingandsomemachinelearningexperiencetogetstarted.

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。