Deeplearningsimplifiedbytakingsupervised,unsupervised,andreinforcementlearningtothenextlevelusingthePythonecosystemTransferlearningisamachinelearning(ML)techniquewhereknowledgegainedduringtrainingasetofproblemscanbeusedtosolveothersimilarproblems.Thepurposeofthisbookistwo-fold;firstly,wefocusondetailedcoverageofdeeplearning(DL)andtransferlearning,comparingandcontrastingthetwowitheasy-to-followconceptsandexamples.Thesecondareaoffocusisreal-worldexamplesandresearchproblemsusingTensorFlow,Keras,andthePythonecosystemwithhands-onexamples.ThebookstartswiththekeyessentialconceptsofMLandDL,followedbydepictionandcoverageofimportantDLarchitecturessuchasconvolutionalneuralnetworks(CNNs),deepneuralnetworks(DNNs),recurrentneuralnetworks(RNNs),longshort-termmemory(LSTM),andcapsulenetworks.Ourfocusthenshiftstotransferlearningconcepts,suchasmodelfreezing,fine-tuning,pre-trainedmodelsincludingVGG,inception,ResNet,andhowthesesystemsperformbetterthanDLmodelswithpracticalexamples.Intheconcludingchapters,wewillfocusonamultitudeofreal-worldcasestudiesandproblemsassociatedwithareassuchascomputervision,audioanalysisandnaturallanguageprocessing(NLP).Bytheendofthisbook,youwillbeabletoimplementbothDLandtransferlearningprinciplesinyourownsystems.WhatyouwilllearnSetupyourownDLenvironmentwithgraphicsprocessingunit(GPU)andCloudsupportDelveintotransferlearningprincipleswithMLandDLmodelsExplorevariousDLarchitectures,includingCNN,LSTM,andcapsulenetworksLearnaboutdataandnetworkrepresentationandlossfunctionsGettogripswithmodelsandstrategiesintransferlearningWalkthroughpotentialchallengesinbuildingcomplextransferlearningmodelsfromscratchExplorereal-worldresearchproblemsrelatedtocompute

Simulationsimplemodelsandcomparisonwithqueueingtheory.AndreiDorokhovThreesimplequeueingmodelsM/M/1,M/G/1andseriesofM/M/1havebeensimulated,andcomparedwithcalculationsbasedonqueueingtheoryforvalidationpurposes.

Today,machinelearningunderliesarangeofapplicationsweuseeveryday,fromproductrecommendationstovoicerecognition--aswellassomewedon'tyetuseeveryday,includingdriverlesscars.Itisthebasisofthenewapproachincomputingwherewedonotwriteprogramsbutcollectdata;theideaistolearnthealgorithmsforthetasksautomaticallyfromdata.Ascomputingdevicesgrowmoreubiquitous,alargerpartofourlivesandworkisrecordeddigitally,andas"BigData"hasgottenbigger,thetheoryofmachinelearning--thefoundationofeffortstoprocessthatdataintoknowledge--hasalsoadvanced.Inthisbook,machinelearningexpertEthemAlpaydinoffersaconciseoverviewofthesubjectforthegeneralreader,describingitsevolution,explainingimportantlearningalgorithms,andpresentingexampleapplications.Alpaydinoffersanaccountofhowdigitaltechnologyadvancedfromnumber-crunchingmainframestomobiledevices,puttingtoday'smachinelearningboomincontext.Hedescribesthebasicsofmachinelearningandsomeapplications;theuseofmachinelearningalgorithmsforpatternrecognition;artificialneuralnetworksinspiredbythehumanbrain;algorithmsthatlearnassociationsbetweeninstances,withsuchapplicationsascustomersegmentationandlearningrecommendations;andreinforcementlearning,whenanautonomousagentlearnsactsoastomaximizerewardandminimizepenalty.Alpaydinthenconsiderssomefuturedirectionsformachinelearningandthenewfieldof"datascience,"anddiscussestheethicalandlegalimplicationsfordataprivacyandsecurity.TableofContentsChapter1WhyWeAreInterestedInMachineLearningChapter2MachineLearning,Statistics,AndDataAnalyticsChapter3PatternRecognitionChapter4NeuralNetworksAndDeepLearningChapter5LearningClustersAndRecommendationsChapter6LearningToTakeActionsChapter7WhereDoWeGoFromHere?

It’sbeenalmosteightyearssinceIfirstupdatedAdvancedProgra妹妹ingintheUNIXEnvironment,andalreadysomuchhaschanged.•Beforethesecondeditionwaspublished,TheOpenGroupcreateda2004editionoftheSingleUNIXSpecification,foldinginthechangesfromtwosetsofcorrigenda.In2008,TheOpenGroupcreatedanewversionoftheSingleUNIXSpecification,updatingthebasedefinitions,addingnewinterfaces,andremovingobsoleteones.Thiswascalledthe2008versionofPOSIX.1,whichincludedversion7oftheBaseSpecificationandwaspublishedin2009.In2010,thiswasbundledwithanupdatedcursesinterfaceandreissuedasversion4oftheSingleUNIXSpecification.•Versions10.5,10.6,and10.8oftheMacOSXoperatingsystem,runningonIntelprocessors,havebeencertifiedtobeUNIX®systemsbyTheOpenGroup.•AppleComputerdiscontinueddevelopmentofMacOSXforthePowerPCplatform.FromRelease10.6(SnowLeopard)onward,newoperatingsystemversionsarereleasedforthex86platformonly.•TheSolarisoperatingsystemwasreleasedinopensourceformtotrytocompetewiththepopularityoftheopensourcemodelfollowedbyFreeBSD,Linux,andMacOSX.AfterOracleCorporationboughtSunMicrosystemsin2010,itdiscontinuedthedevelopmentofOpenSolaris.Instead,theSolarisco妹妹unityformedtheIllumosprojecttocontinueopensourcedevelopmentbasedonOpenSolaris.Formoreinformation,seehttp://www.illumos.org.

（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。

雅虎研究院、计算广告鼻祖AndreiBroder关于计算广告的一个幻灯片，相信对计算广告有了解的同学应该都晓得这个人。

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。