Theacceleratingdeploymentoflarge-scaleweb,cloud,BigData,andvirtualizedcomputingsystemshasintroducedseriousnewchallengesinperformanceoptimization.Untilnow,however,littlereliable,practicalinformationhasbeenavailabletoITprofessionalswhoareresponsibleforrunningthesesystemsefficientlyandcost-effectively.SystemsPerformance:EnterpriseandtheCloudisthesolution.InternationallyrenownedperformanceoptimizationexpertBrendanGreggbringstogetherstate-of-the-arttechniquesandtoolsforanalysisandtuningoflarge-scaleweb/cloudcomputingenvironments.GreggfocusesonLinux/Unix/Solarisperformanceissues,whileofferingprovenmethodologiesanddiscussingkeyissuesthatapplytoallenterpriseoperatingsystems.Coverageincludes:Modernperformanceanalysisandcapacityplanning,includingkeyissuessuchaslatencyanddynamictracingNewperformanceandreliabilitychallengesassociatedwithcloudcomputingMethodology,concepts,terminology,tools,andmetricsKeytradeoffs,includingproblemsofloadvs.architectureTuningoperatingsystems,CPUs,memory,filesystems,disks,networks,andbussesTuningvirtualizedsystemsProgramminglanguageissuesrelatedtoperformance—includingapplicationprofilingforC,C++,Java,andnode.jsBenchmarkingstrategiesandpitfalls,includingcustommicrobenchmarking

BigData_SidePro_NewBigData_SidePro_New

华为HCNA-BigData(H13-711)题库，word版本，更新至20180816

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?

大数据bigdata可视化UI样例htmlDemo，可提取样式、规划、控件等。
大数据bigdata可视化UI样例htmlDemo，可提取样式、规划、控件等。

BigData：ARevolutionThatWillTransformhowwelive，work，andthink作者：维克托·迈尔-舍恩伯格（ViktorMayer-Schönberger）

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。