ProbabilisticFoundationsofStatisticalNetworkAnalysispresentsafreshandinsightfulperspectiveonthefundamentaltenetsandmajorchallengesofmodernnetworkanalysis.Itslucidexpositionprovidesnecessarybackgroundforunderstandingtheessentialideasbehindexchangeableanddynamicnetworkmodels,networksampling,andnetworkstatisticssuchassparsityandpowerlaw,allofwhichplayacentralroleincontemporarydatascienceandmachinelearningapplications.Thebookrewardsreaderswithaclearandintuitiveunderstandingofthesubtleinterplaybetweenbasicprinciplesofstatisticalinference,empiricalpropertiesofnetworkdata,andtechnicalconceptsfromprobabilitytheory.Itsmathematicallyrigorous,yetnon-technical,expositionmakesthebookaccessibletoprofessionaldatascientists,statisticians,andcomputerscientistsaswellaspractitionersandresearchersinsubstantivefields.Newcomersandnon-quantitativeresearcherswillfinditsconceptualapproachinvaluablefordevelopingintuitionabouttechnicalideasfromstatisticsandprobability,whileexpertsandgraduatestudentswillfindthebookahandyreferenceforawiderangeofnewtopics,includingedgeexchangeability,relativeexchangeability,graphonandgraphexmodels,andgraph-valuedLevyprocessandrewiringmodelsfordynamicnetworks.Theauthor’sincisivecommentarysupplementsthesecoreconcepts,challengingthereadertopushbeyondthecurrentlimitationsofthisemergingdiscipline.Withanapproachableexpositionandmorethan50openresearchproblemsandexerciseswithsolutions,thisbookisidealforadvancedundergraduateandgraduatestudentsinterestedinmodernnetworkanalysis,datascience,machinelearning,andstatistics.HarryCraneisAssociateProfessorandCo-DirectoroftheGraduatePrograminStatisticsandBiostatisticsandanAssociateMemberoftheGraduateFacultyinPhilosophyatRutgersUniversity.ProfessorCrane’sresea

ProbabilisticFoundationsofStatisticalNetworkAnalysispresentsafreshandinsightfulperspectiveonthefundamentaltenetsandmajorchallengesofmodernnetworkanalysis.Itslucidexpositionprovidesnecessarybackgroundforunderstandingtheessentialideasbehindexchangeableanddynamicnetworkmodels,networksampling,andnetworkstatisticssuchassparsityandpowerlaw,allofwhichplayacentralroleincontemporarydatascienceandmachinelearningapplications.Thebookrewardsreaderswithaclearandintuitiveunderstandingofthesubtleinterplaybetweenbasicprinciplesofstatisticalinference,empiricalpropertiesofnetworkdata,andtechnicalconceptsfromprobabilitytheory.Itsmathematicallyrigorous,yetnon-technical,expositionmakesthebookaccessibletoprofessionaldatascientists,statisticians,andcomputerscientistsaswellaspractitionersandresearchersinsubstantivefields.Newcomersandnon-quantitativeresearcherswillfinditsconceptualapproachinvaluablefordevelopingintuitionabouttechnicalideasfromstatisticsandprobability,whileexpertsandgraduatestudentswillfindthebookahandyreferenceforawiderangeofnewtopics,includingedgeexchangeability,relativeexchangeability,graphonandgraphexmodels,andgraph-valuedLevyprocessandrewiringmodelsfordynamicnetworks.Theauthor’sincisivecommentarysupplementsthesecoreconcepts,challengingthereadertopushbeyondthecurrentlimitationsofthisemergingdiscipline.Withanapproachableexpositionandmorethan50openresearchproblemsandexerciseswithsolutions,thisbookisidealforadvancedundergraduateandgraduatestudentsinterestedinmodernnetworkanalysis,datascience,machinelearning,andstatistics.HarryCraneisAssociateProfessorandCo-DirectoroftheGraduatePrograminStatisticsandBiostatisticsandanAssociateMemberoftheGraduateFacultyinPhilosophyatRutgersUniversity.ProfessorCrane’sresea

ProbabilisticFoundationsofStatisticalNetworkAnalysispresentsafreshandinsightfulperspectiveonthefundamentaltenetsandmajorchallengesofmodernnetworkanalysis.Itslucidexpositionprovidesnecessarybackgroundforunderstandingtheessentialideasbehindexchangeableanddynamicnetworkmodels,networksampling,andnetworkstatisticssuchassparsityandpowerlaw,allofwhichplayacentralroleincontemporarydatascienceandmachinelearningapplications.Thebookrewardsreaderswithaclearandintuitiveunderstandingofthesubtleinterplaybetweenbasicprinciplesofstatisticalinference,empiricalpropertiesofnetworkdata,andtechnicalconceptsfromprobabilitytheory.Itsmathematicallyrigorous,yetnon-technical,expositionmakesthebookaccessibletoprofessionaldatascientists,statisticians,andcomputerscientistsaswellaspractitionersandresearchersinsubstantivefields.Newcomersandnon-quantitativeresearcherswillfinditsconceptualapproachinvaluablefordevelopingintuitionabouttechnicalideasfromstatisticsandprobability,whileexpertsandgraduatestudentswillfindthebookahandyreferenceforawiderangeofnewtopics,includingedgeexchangeability,relativeexchangeability,graphonandgraphexmodels,andgraph-valuedLevyprocessandrewiringmodelsfordynamicnetworks.Theauthor’sincisivecommentarysupplementsthesecoreconcepts,challengingthereadertopushbeyondthecurrentlimitationsofthisemergingdiscipline.Withanapproachableexpositionandmorethan50openresearchproblemsandexerciseswithsolutions,thisbookisidealforadvancedundergraduateandgraduatestudentsinterestedinmodernnetworkanalysis,datascience,machinelearning,andstatistics.HarryCraneisAssociateProfessorandCo-DirectoroftheGraduatePrograminStatisticsandBiostatisticsandanAssociateMemberoftheGraduateFacultyinPhilosophyatRutgersUniversity.ProfessorCrane’sresea

Datascienceisaninterdisciplinaryfieldencompassingscientificmethods,processes,andsystemstoextractknowledgeorinsightsfromdatainvariousforms,eitherstructuredorunstructured.Itdrawsprinciplesfrommathematics,statistics,informationscience,computerscience,machinelearning,visualization,datamining,andpredictiveanalytics.However,itisfundamentallygroundedinmathematics.ThisbookexplainsandappliesthefundamentalsofdatasciencecrucialfortechnicalprofessionalssuchasDBAsanddeveloperswhoaremakingcareermovestowardpracticingdatascience.Itisanexample-drivenbookprovidingcompletePythoncodingexamplestocomplementandclarifydatascienceconcepts,andenrichthelearningexperience.Codingexamplesincludevisualizationswheneverappropriate.Thebookisanecessaryprecursortoapplyingandimplementingmachinelearningalgorithms,becauseitintroducesthereadertofoundationalprinciplesofthescienceofdata.

使用适用于STM32F407uC的不同框架的TinyML警告：由于库包含在.gitignore中，因此必须为每个项目都生成CubeMX代码。
对于TFLite项目，必须将自动生成的main更改为.cpp，并且必须将其与main.c.中的最新更改合并。
开发IDE：KeiluVisionv6多维数据集MXAI/003MagicWand在CubeMXAI\003MagicWand\Middlewares\Third_Party\MachineLearning\edgeimpulse\model-parameters\model_metadata.h中修改以下参数：#defineEI_CLASSIFIER_RAW_SAMPLE_COUNT200#defineEI_CLASSIFIER_INTERVAL_MS10#defineEI_

2018/2019/校招/春招/秋招/自然语言处理(NLP)/深度学习(DeepLearning)/机器学习(MachineLearning)/C/C/Python/面试笔记

分为作业和答案两部分，分开的资源，不用因为有答案影响自己的思路

首先，来看看CSDN的知识图谱http://lib.csdn.net/base/machinelearning/structure万般喜欢这个插件，于是就翻看源代码，抄了下来

UCBMichaelJordan写的introductiontographicalmodel,入门级介绍。
做machinelearning的同学可以看看。
一共12章，未出版手稿

Title:MachineLearning:AnAlgorithmicPerspective,2ndEditionAuthor:StephenMarslandLength:457pagesEdition:2Language:EnglishPublisher:ChapmanandHall/CRCPublicationDate:2014-10-08ISBN-10:1466583282ISBN-13:9781466583283AProven,Hands-OnApproachforStudentswithoutaStrongStatisticalFoundationSincethebest-sellingfirsteditionwaspublished,therehavebeenseveralprominentdevelopmentsinthefieldofmachinelearning,includingtheincreasingworkonthestatisticalinterpretationsofmachinelearningalgorithms.Unfortunately,computersciencestudentswithoutastrongstatisticalbackgroundoftenfindithardtogetstartedinthisarea.Remedyingthisdeficiency,MachineLearning:AnAlgorithmicPerspective,SecondEditionhelpsstudentsunderstandthealgorithmsofmachinelearning.Itputsthemonapathtowardmasteringtherelevantmathematicsandstatisticsaswellasthenecessaryprogrammingandexperimentation.NewtotheSecondEditionTwonewchaptersondeepbeliefnetworksandGaussianprocessesReorganizationofthechapterstomakeamorenaturalflowofcontentRevisionofthesupportvectormachinematerial,includingasimpleimplementationforexperimentsNewmaterialonrandomforests,theperceptronconvergencetheorem,accuracymethods,andconjugategradientoptimizationforthemulti-layerperceptronAdditionaldiscussionsoftheKalmanandparticlefiltersImprovedcode,includingbetteruseofnamingconventionsinPythonSuitableforbothanintroductoryone-semestercourseandmoreadvancedcourses,thetextstronglyencouragesstudentstopracticewiththecode.Eachchapterincludesdetailedexamplesalongwithfurtherreadingandproblems.Allofthecodeusedtocreatetheexamplesisavailableontheauthor’swebsite.TableofContentsChapter1:IntroductionChapter2:PreliminariesChapter3:Neurons,NeuralNetworks,andLinearDiscriminantsChapter4:TheMulti-layerPerceptronChapter5:R

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。