斯坦福机器学习编程作业machine-learning-ex3，Multi-classClassicationandNeuralNetworks神经网络模型，非线性模型题目，满分，2015最新作业答案Multi-classClassicationandNeuralNetworksMATLAB

threejs开发指南的源码，可以作为入门threejs的基础代码，

Haihua_competition海华2021年AI竞赛的基准代码比赛链接：（专业组）（学生组）如何使用基线：安装所需的软件包pipinstallrequirements将train.json和validation.json移到与代码相同的目录中pythonpreprocessing.pypythonrun_base.py--task_namerc--do_train--do_eval--data_dir.--init_checkpointhfl/chinese-bert-wwm-ext--max_seq_length512--train_batch_size16--eval_batch_size8--learning_rate1e-5--num_train_epochs5--output_dirmrc_b

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HandsOnReinforcementLearningwithPythonepub版本

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

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StructuredStreaming是一个可拓展，容错的，基于SparkSQL执行引擎的流处理引擎。
使用小量的静态数据模拟流处理。
伴随流数据的到来，SparkSQL引擎会逐渐连续处理数据并且更新结果到最终的Table中。
你可以在SparkSQL上引擎上使用DataSet/DataFrameAPI处理流数据的聚集，事件窗口，和流与批次的连接操作等。
最后StructuredStreaming系统快速，稳定，端到端的恰好一次保证，支持容错的处理。

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?

对深度强化学习的基本操作的程序。

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。