Thistextbookintroducessparseandredundantrepresentationswithafocusonapplicationsinsignalandimageprocessing.Thetheoreticalandnumericalfoundationsaretackledbeforetheapplicationsarediscussed.Mathematicalmodelingforsignalsourcesisdiscussedalongwithhowtousethepropermodelfortaskssuchasdenoising,restoration,separation,interpolationandextrapolation,compression,sampling,analysisandsynthesis,detection,recognition,andmore.Thepresentationiselegantandengaging.SparseandRedundantRepresentationsisintendedforgraduatestudentsinappliedmathematicsandelectricalengineering,aswellasappliedmathematicians,engineers,andresearcherswhoareactiveinthefieldsofsignalandimageprocessing.*Introducestheoreticalandnumericalfoundationsbeforetacklingapplications*Discusseshowtousethepropermodelforvarioussituations*Introducessparseandredundantrepresentations*FocusesonapplicationsinsignalandimageprocessingThefieldofsparseandredundantrepresentationmodelinghasgonethroughamajorrevolutioninthepasttwodecades.Thisstartedwithaseriesofalgorithmsforapproximatingthesparsestsolutionsoflinearsystemsofequations,latertobefollowedbysurprisingtheoreticalresultsthatguaranteethesealgorithms’performance.Withthesecontributionsinplace,majorbarriersinmakingthismodelpracticalandapplicablewereremoved,andsparsityandredundancybecamecentral,leadingtostate-of-the-artresultsinvariousdisciplines.Oneofthemainbeneficiariesofthisprogressisthefieldofimageprocessing,wherethismodelhasbeenshowntoleadtounprecedentedperformanceinvariousapplications.Thisbookprovidesacomprehensiveviewofthetopicofsparseandredundantrepresentationmodeling,anditsuseinsignalandimageprocessing.Itoffersasystematicandorderedexposuretothetheoreticalfoundationsofthisdatamodel,thenumericalaspec

清晰彩色Whenmostpeoplehear“MachineLearning,”theypicturearobot:adependablebutleroradeadlyTerminatordependingonwhoyouask.ButMachineLearningisnotjustafuturisticfantasy,it’salreadyhere.Infact,ithasbeenaroundfordecadesinsomespecializedapplications,suchasOpticalCharacterRecognition(OCR).ButthefirstMLapplicationthatreallybecamemainstream,improvingthelivesofhundredsofmillionsofpeople,tookovertheworldbackinthe1990s:itwasthespamfilter.Notexactlyaself-awareSkynet,butitdoestechnicallyqualifyasMachineLearning(ithasactuallylearnedsowellthatyouseldomneedtoflaganemailasspamanymore).ItwasfollowedbyhundredsofMLapplicationsthatnowquietlypowerhundredsofproductsandfeaturesthatyouuseregularly,frombetterrecommendationstovoicesearch.

face_recognition（人脸识别），face_recognition（人脸识别），供大家参考。

使用的是opencv3.4.1版本。
face_recognition可以使用apt-getinstall安装，这也是为什么用ubuntu的原因。
有问题欢迎留言讨论。

机器学习经典书籍，PRML，PatternRecognitionAndMachineLearning，和课后习题答案

纵观《PRML》（《PatternRecognitionandMachineLearning》），讲述解决问题的方法的只有20%不到的内容，而其余的内容都是在讲述怎样将问题解决得更好（精度更高）！模式识别逃不掉的一本书，还出了中文版！！

FaceRecognition是一个基于Python的人脸识别库，它还提供了一个命令行工具，让你通过命令行对任意文件夹中的图像进行人脸识别操作。
该库使用dlib顶尖的深度学习人脸识别技术构建，在户外脸部检测数据库基准(LabeledFacesintheWildbenchmark)上的准确率高达99.38%。

LearnhowtomodelandtrainadvancedneuralnetworkstoimplementavarietyofComputerVisiontasksKeyFeaturesTraindifferentkindsofdeeplearningmodelfromscratchtosolvespecificproblemsinComputerVisionCombinethepowerofPython,Keras,andTensorFlowtobuilddeeplearningmodelsforobjectdetection,imageclassification,similaritylearning,imagecaptioning,andmoreIncludestipsonoptimizingandimprovingtheperformanceofyourmodelsundervariousconstraintsBookDescriptionDeeplearninghasshownitspowerinseveralapplicationareasofArtificialIntelligence,especiallyinComputerVision.ComputerVisionisthescienceofunderstandingandmanipulatingimages,andfindsenormousapplicationsintheareasofrobotics,automation,andsoon.Thisbookwillalsoshowyou,withpracticalexamples,howtodevelopComputerVisionapplicationsbyleveragingthepowerofdeeplearning.Inthisbook,youwilllearndifferenttechniquesrelatedtoobjectclassification,objectdetection,imagesegmentation,captioning,imagegeneration,faceanalysis,andmore.YouwillalsoexploretheirapplicationsusingpopularPythonlibrariessuchasTensorFlowandKeras.Thisbookwillhelpyoumasterstate-of-the-art,deeplearningalgorithmsandtheirimplementation.WhatyouwilllearnSetupanenvironmentfordeeplearningwithPython,TensorFlow,andKerasDefineandtrainamodelforimageandvideoclassificationUsefeaturesfromapre-trainedConvolutionalNeuralNetworkmodelforimageretrievalUnderstandandimplementobjectdetectionusingthereal-worldPedestrianDetectionscenarioLearnaboutvariousproblemsinimagecaptioningandhowtoovercomethembytrainingimagesandtexttogetherImplementsimilaritymatchingandtrainamodelforfacerecognitionUnderstandtheconceptofgenerativemodelsandusethemforimagegenerationDeployyourdeeplearningmodelsandoptimizethemforhighperformanceWhoThisBookIsForThisbookistargeted

基于树莓派的人脸识别，可用来制作智能门锁

PatternRecognitionandMachineLearning.Bishop.英文版完整版pdf

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。