本资料包专为AI学习者打造，包含37.4GB精选付费内容，涵盖人工智能行业报告、必读经典书籍、机器学习与深度学习算法教程、计算机视觉实战项目等。无论您是初学者还是进阶者，都能从中获取实用的学习资源。资料包内含OpenCV、YOLOV5、MASK-RCNN、Unet等实战视频课程及配套资料，助您快速掌握AI核心技术。此外，还提供超详细的人工智能学习大纲和论文合集，帮助您系统化学习并紧跟行业前沿。立即下载，开启您的AI学习之旅！
以下云资源目录树快照生成于[1年前]，该学习资料由夸克云用户[KK*5525]分享（只展示大部分文件和目录）
37.4GB AI精选付费资料包：从入门到实战25.47 GB(mp4、flv视频253节；png、jpg图片51张；pdf、docx、txt文档214个；zip、rar压缩包37个；pptx演示文稿1个；)
一：人工智能论文合集
图神经网络（GNN）100篇论文集
论文集索引.jpg29.73KBSurvey
一般推荐
NeuralMessagePassingforQuantumChemistry.pdf511.15KBGeometricDeepLearning-GoingbeyondEuclideandata.pdf5.26MBDeepLearningonGraphs-ASurvey.pdf1.8MBComputationalCapabilitiesofGraphNeuralNetworks(1).pdf1.28MBAComprehensiveSurveyonGraphNeuralNetworks.pdf1.8MB极力推荐
TheGraphNeuralNetworkModel.pdf1.43MBRelationalInductiveBiases,DeepLearning,andGraphNetworks.pdf8.99MBNon-localNeuralNetworks.pdf1.24MBGraphNeuralNetworks：AReviewofMethodsandApplications.pdf2.67MBModels
trainingmethods
Neuralnetworksforrelationallearning-anexperimentalcomparison.pdf1.15MBLearningSteady-StatesofIterativeAlgorithmsoverGraphs.pdf3.09MBKnowledge-GuidedRecurrentNeuralNetworkLearningforTask-OrientedActionPrediction.pdf1000.46KBHierarchicalGraphRepresentationLearningwithDifferentiablePooling.pdf2.31MBGraphical-BasedLearningEnvironmentsforPatternRecognition.pdf335.92KBCovariantCompositionalNetworksForLearningGraphs.pdf482.53KBreceptivefieldcontrol
StochasticTrainingofGraphConvolutionalNetworkswithVarianceReduction.pdf1.25MBneighborhoodsampling
InductiveRepresentationLearningonLargeGraphs.pdf1.04MBFastGCN-FastLearningwithGraphConvolutionalNetworksviaImportanceSampling.pdf358.35KBAdaptiveSamplingTowardsFastGraphRepresentationLearning.pdf579.95KBboosting
DeeperInsightsintoGraphConvolutionalNetworksforSemi-SupervisedLearning.pdf1.96MBpropagation_type
skip
Semi-SupervisedClassificationwithGraphConvolutionalNetworks.pdf853.42KBRepresentationLearningonGraphswithJumpingKnowledgeNetworks.pdf3.15MBgate
Sentence-StateLSTMforTextRepresentation.pdf442.27KBGatedGraphSequenceNeuralNetworks.pdf748.16KBconvolution
Structure-AwareConvolutionalNeuralNetworks.pdf1.36MBSpectralNetworksandDeepLocallyConnected.pdf1.86MBLearningConvolutionalNeuralNetworksforGraphs.pdf639.85KBDeepConvolutionalNetworksonGraph-StructuredData.pdf4.57MBConvolutionalNeuralNetworksonGraphswithFastLocalizedSpectralFiltering.pdf459.44KBBayesianSemi-supervisedLearningwithGraphGaussianProcesses.pdf689.89KBattention
GraphClassificationusingStructuralAttention.pdf2.47MBGraphAttentionNetworks.pdf1.48MBAttentionIsAllYouNeed.pdf2.1MBothers
Geometricdeeplearningongraphsandmanifoldsusingmixturemodelcnns.pdf7.23MBDiffusion-ConvolutionalNeuralNetworks.pdf366.35KBDerivingNeuralArchitecturesfromSequenceandGraphKernels.pdf687.05KBDeepSets.pdf5.11MBContextualGraphMarkovModel-ADeepandGenerativeApproachtoGraphProcessing.pdf570.59KBCelebrityNet-ASocialNetworkConstructedfromLarge-ScaleOnlineCelebrityImages.pdf16.33MBAnewmodelforlearningingraphdomains.pdf177.61KBAComparisonbetweenRecursiveNeuralNetworksandGraphNeuralNetworks.pdf247.2KBgraph_type
Mean-fieldtheoryofgraphneuralnetworksingraphpartitioning.pdf369.44KBHowPowerfulareGraphNeuralNetworks-.pdf678.3KBGraphPartitionNeuralNetworksforSemi-SupervisedClassification.pdf713.9KBGraphNeuralNetworksforRankingWebPages.pdf1.01MBGraphNeuralNetworksforObjectLocalization.pdf221.83KBGraphCapsuleConvolutionalNeuralNetworks.pdf1.93MBAdaptiveGraphConvolutionalNeuralNetworks.pdf803.92KBedge-informativegraph
Modelingrelationaldatawithgraphconvolutionalnetworks.pdf323.62KBGraph-to-SequenceLearningusingGatedGraphNeuralNetworks.pdf4.06MBdirectedgraph
RethinkingKnowledgeGraphPropagationforZero-ShotLearning.pdf4.21MBApplications
text
RecurrentRelationalNetworks.pdf307KBN-aryrelationextractionusinggraphstateLSTM.pdf455.67KBJointlyMultipleEventsExtractionviaAttention-basedGraph.pdf430.38KBGraphConvolutionalNetworkswithArgument-AwarePoolingforEventDetection.pdf324.7KBGraphConvolutionalNetworksforTextClassification.pdf1.83MBGraphConvolutionalEncodersforSyntax-awareNeuralMachineTranslation.pdf346.9KBGraphConvolutionoverPrunedDependencyTreesImprovesRelationExtraction.pdf784.41KBExploringGraph-structuredPassageRepresentationforMulti-hopReadingComprehensionwithGraphNeuralNetworks..pdf453.5KBExploitingSemanticsinNeuralMachineTranslationwithGraphConvolutionalNetworks.pdf604.59KBEnd-to-EndRelationExtractionusingLSTMsonSequencesandTreeStructures.pdf363.06KBEncodingSentenceswithGraphConvolutionalNetworksforSemanticRoleLabeling.pdf621.87KBAGraph-to-SequenceModelforAMR-to-TextGeneration.pdf290.15KBscience
VisualInteractionNetworks-LearningaPhysicsSimulatorfromVide.o.pdf5.41MBVAIN-AttentionalMulti-agentPredictiveModeling.pdf423.97KBUnderstandingKinRelationshipsinaPhoto.pdf1.44MBTranslatingEmbeddingsforModelingMulti-relationalData.pdf414.17KBTrafficGraphConvolutionalRecurrentNeuralNetwork-ADeepLearningFrameworkforNetwork-ScaleTrafficLearningandForecasting.pdf1.45MBSymbolicGraphReasoningMeetsConvolutions.pdf3.23MBStructuredDialoguePolicywithGraphNeuralNetworks.pdf779.24KBSpatio-TemporalGraphConvolutionalNetworks-ADeepLearningFrameworkforTrafficForecasting.pdf895.05KBSpatialTemporalGraphConvolutionalNetworksforSkeleton-BasedActionRecognition.pdf1.5MBSituationRecognitionwithGraphNeuralNetworks.pdf5.27MBSemi-supervisedUserGeolocationviaGraphConvolutionalNetworks.pdf1.13MBSelf-AttentionwithRelativePositionRepresentations.pdf229.86KBRelationalneuralexpectationmaximization-Unsuperviseddiscoveryofobjectsandtheirinteractions.pdf1.15MBRelationalinductivebiasforphysicalconstructioninhumansandmachines.pdf1022.51KBRelationalDeepReinforcementLearning.pdf6.81MBProteinInterfacePredictionusingGraphConvolutionalNetworks.pdf837.75KBNeuralRelationalInferenceforInteractingSystems.pdf2.83MBNeuralModuleNetworks.pdf1.03MBNeuralCombinatorialOptimizationwithReinforcementLearning.pdf393.17KBNerveNetLearningStructuredPolicywithGraphNeuralNetworks.pdf3.11MBMolecularGraphConvolutions-MovingBeyondFingerprints.pdf2.08MBMetacontrolforAdaptiveImagination-BasedOptimization.pdf1.6MBLearningtoRepresentProgramswithGraphs.pdf421.9KBLearningMultiagentCommunicationwithBackpropagation.pdf4.13MBLearningmodel-basedplanningfromscratch.pdf1.28MBLearningHuman-ObjectInteractionsbyGraphParsingNeuralNetworks.pdf3.91MBLearningGraphicalStateTransitions.pdf1.47MBLearningDeepGenerativeModelsofGraphs.pdf2.31MBLearningConditionedGraphStructuresforInterpretableVisualQuestionAnswering.pdf8.48MBLearningaSATSolverfromSingle-BitSupervision.pdf1.89MBInteractionNetworksforLearningaboutObjects,RelationsandPhysics.pdf1.91MBInferenceinProbabilisticGraphicalModelsbyGraphNeuralNetworks.pdf3.07MBImprovedSemanticRepresentationsFromTree-StructuredLongShort-TermMemoryNetworks.pdf304.16KBHyperbolicAttentionNetworks.pdf3.08MBHybridApproachofRelationNetworkandLocalizedGraphConvolutionalFilteringforBreastCancerSubtypeClassification.pdf2.52MBGraphRNN-GeneratingRealisticGraphswithDeepAuto-regressiveModels.pdf2.43MBGraphnetworksaslearnablephysicsenginesforinferenceandcontrol.pdf2.72MBGraphConvolutionalNeuralNetworksforWeb-ScaleRecommenderSystems.pdf9.84MBGraphConvolutionalMatrixCompletion.pdf732.99KBGeometricMatrixCompletionwithRecurrentMulti-GraphNeuralNetworks.pdf6.99MBEffectiveApproachestoAttention-basedNeuralMachineTranslation.pdf243.97KBDynamicEdge-ConditionedFiltersinConvolutionalNeuralNetworksonGraphs.pdf567.07KBDiscoveringobjectsandtheirrelationsfromentangledscenerepresentations.pdf4.99MBDeepInf-Modelinginfluencelocalityinlargesocialnetworks.pdf1.07MBDeepGraphInfomax.pdf8.15MBCross-SentenceN-aryRelationExtractionwithGraphLSTMs.pdf540.89KBConvolutionalnetworksongraphsforlearningmolecularfingerprints.pdf785.36KBConversationModelingonRedditusingaGraph-StructuredLSTM.pdf682.35KBConstructingNarrativeEventEvolutionaryGraphforScriptEventPrediction.pdf654.87KBConstrainedGenerationofSemanticallyValidGraphsviaRegularizingVariationalAutoencoders.pdf567.14KBCombiningNeuralNetworkswithPersonalizedPageRankforClassificationonGraphs.pdf483.25KBBeyondCategories-TheVisualMemexModelforReasoningAboutObjectRelationships.pdf618.71KBAttention,LearntoSolveRoutingProblems!.pdf1.48MBAttend,Infer,Repeat-FastSceneUnderstandingwithGenerativeModels.pdf1.3MBAdversarialAttackonGraphStructuredData.pdf593.12KBActionSchemaNetworks-GeneralisedPolicieswithDeepLearning.pdf1.67MBAsimpleneuralnetworkmoduleforrelationalreasoning.pdf1.37MBANoteonLearningAlgorithmsforQuadraticAssignmentwithGraphNeuralNetworks.pdf340.4KBACompositionalObject-BasedApproachtoLearningPhysicalDynamics.pdf4.26MBknowledgegraph
Zero-shotRecognitionviaSemanticEmbeddingsandKnowledgeGraphs.pdf1.63MBTheMoreYouKnow-UsingKnowledgeGraphsforImageClassification.pdf2.31MBRepresentationlearningforvisual-relationalknowledgegraphs.pdf6.9MBMulti-LabelZero-ShotLearningwithStructuredKnowledgeGraphs.pdf1.36MBModelingSemanticswithGatedGraphNeuralNetworksforKnowledgeBaseQuestionAnswering.pdf437.8KBKnowledgeTransferforOut-of-Knowledge-BaseEntities-AGraphNeuralNetworkApproach.pdf355.22KBDynamicGraphGenerationNetwork-GeneratingRelationalKnowledgefromDiagrams.pdf1.19MBDeepReasoningwithKnowledgeGraphforSocialRelationshipUnderstanding.pdf2.76MBCross-lingualKnowledgeGraphAlignmentviaGraphConvolutionalNetworks.pdf432.63KBimage
VisualQuestionAnswering
OutoftheBox-ReasoningwithGraphConvolutionNetsforFactualVisualQuestionAnswering(1).pdf2.45MBGraph-StructuredRepresentationsforVisualQuestionAnswering.pdf3.74MBSemanticSegmentation
PointNet-DeepLearningonPointSetsfor3DClassificationandSegmentation.pdf8.66MBModelingpolypharmacysideeffectswithgraphconvolutionalnetworks.pdf4.18MBLarge-scalePointCloudSemanticSegmentationwithSuperpointGraphs.pdf4.83MBDynamicGraphCNNforLearningonPointClouds.pdf5.07MB3DGraphNeuralNetworksforRGBDSemanticSegmentation.pdf2.23MBRegionClassification
IterativeVisualReasoningBeyondConvolutions..pdf3.91MBObjectDetection
RelationNetworksforObjectDetection.pdf906.66KBLearningRegionfeaturesforObjectDetection.pdf1.68MBInteractionDetection
Structural-RNN-DeepLearningonSpatio-TemporalGraphs.pdf1.1MBImageclassification
Few-ShotLearningwithGraphNeuralNetworks.pdf1.69MBgraphgeneration
NetGAN-GeneratingGraphsviaRandomWalks(1).pdf1.67MBMolGAN-Animplicitgenerativemodelforsmallmoleculargraphs(1).pdf1.1MBGraphConvolutionalPolicyNetworkforGoal-DirectedMolecularGraphGeneration.pdf517.97KBcombinatorialoptimization
LearningCombinatorialOptimizationAlgorithmsoverGraphs.pdf2.91MBCombinatorialOptimizationwithGraphConvolutionalNetworksandGuidedTreeSearch(1).pdf537.04KB深度学习论文精讲-BERT模型
9.8-论文总结分析.mp476.41MB8.7-BERT模型训练策略.mp442.95MB7.6-向量特征编码方法.mp424.65MB6.5-输入数据特殊编码字符解析.mp443.88MB5.4-预训练模型的作用.mp418.43MB4.3-模型在NLP领域应用效果.mp433.56MB3.2-BERT模型摘要概述.mp432.28MB2.1-论文讲解思路概述.mp414.77MB1.课程介绍.mp436.9MBResnet论文解读
13-额外补充-Resnet论文解读.mp4117.38MBICCV2021
解压密码：iccv2021
CVPR行人重识别论文解读
6.5-图卷积模块实现方法.mp423.49MB5.4-基于图卷积构建人体拓扑关系.mp425.86MB4.3-局部特征热度图计算.mp421.11MB2.2-图卷积与匹配的作用.mp420.81MB1.1-关键点位置特征构建.mp417.96MBcvpr2021
解压密码：cvpr2021
CNN_不能错过的10篇论文
Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf1.24MB4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf1.35MB1512.03385v1_DeepResidualLearningforImageRecognition.pdf800.18KB1506.02025_SpatialTransformerNetworks.pdf7.89MB1506.01497v3_FasterR-CNN.pdf6.59MB1504.08083_FastR-CNN.pdf713.99KB1412.2306v2_DeepVisual-SemanticAlignmentsforGeneratingImageDescriptions.pdf5.21MB1409.1556v6_VERYDEEPCONVOLUTIONALNetworks.pdf195.32KB1406.2661v1_GenerativeAdversarialNets.pdf518.05KB1311.2901v3_VisualizingandUnderstandingConvolutionalNetworks.pdf34.56MB1311.2524v5_R_CNN.pdf6.23MB五：深度学习神经网络基础教程
神经网络模型基础课件资料
Deep-Learning-with-PyTorch-Tutorials.zip80.87MBCNN+RNN+GAN
源代码和PPT在Github下载.txt72Byte课程安装软件-Win10
pycharm-community-2019.1.1.exe231.79MBcudnn-10.0-windows10-x64-v7.5.0.56(1).zip213.78MBcuda_10.0.130_411.31_win10.exe2.04GBAnaconda3-2019.03-Windows-x86_64.exe661.66MB课程安装软件-Ubuntu18.04
cudnn-10.0-linux-x64-v7.5.0.56.tgz412.76MBcuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb1.55GBAnaconda3-2019.03-Linux-x86_64.sh654.13MBRNN循环神经网络基础
9.课时9LSTM中Layer的使用.mp412.17MB8.课时8LSTM基本原理-2.mp416.19MB7.课时7LSTM基本原理-1.mp412.95MB6.课时6项目实战-时间序列预测问题.mp419.59MB5.课时5循环神经网络中Layer的使用-2.mp414.44MB4.课时4循环神经网络中Layer使用-1.mp416.04MB3.课时3循环神经网络基本原理-2.mp416.3MB2.课时2循环神经网络基本原理-1.mp413.68MB11.课时11项目实战-情感分类问题.mp433.45MB10.课时10RNN训练难题—梯度弥散与梯度爆炸.mp423.86MB1.课时1时间序列介绍.mp421.59MBGAN对抗生成网络基础
9GAN实战-1.flv16.82MB8WGAN-GP原理.flv30.63MB7EM距离.flv19.16MB6GAN训练难题.flv37.04MB5纳什均衡-2.flv35.39MB4纳什均衡-1.flv19.17MB3生成对抗网络.flv25.78MB2画家的成长历程.flv29.46MB12WGAN实战-2.flv35.24MB11WGAN实战-1.flv19.71MB10GAN实战-2.flv33.53MB1数据的分布.flv17.67MBCNN卷积神经网络基础
9-池化与采样操作讲解.mp417.02MB8-卷积神经网络图解-4.mp426.54MB7-卷积神经网络图解-3.mp425.8MB6-卷积神经网络图解-2.mp430.4MB5-卷积神经网络图解-1.mp436.88MB4-卷积运算详解-4.mp421.91MB3-卷积运算详解-3.mp428.37MB2-卷积运算详解-2.mp427.98MB23-ResNet实战-4.mp417.54MB22-ResNet实战-3.mp415.11MB21-ResNet实战-2.mp414.52MB20-ResNet实战-1.mp414.3MB1-卷积运算详解-1.mp430.81MB19-ResNet,DenseNet详解.mp418.99MB18-ResNet,DenseNet详解.mp418.06MB17-BatchNorm-2.mp430.27MB15-经典卷积神经网络详解-2.mp413.17MB14-经典卷积神经网络详解-1.mp416.68MB13-CIFAR100与VGG13实战-4.mp410.4MB12-CIFAR100与VGG13实战-3.mp414.83MB11-CIFAR100与VGG13实战-2.mp413.69MB10-CIFAR100与VGG13实战-1.mp413.99MB四：机器学习基础算法教程
02.机器学习算法课件资料
机器学习算法PPT
文本分析.pdf522.2KB时间序列分析.pdf767.26KB9-LDA与PCA算法.pdf1.04MB8-xgboost.pdf932.12KB7-推荐系统.pdf1.97MB6-支持向量机.pdf1.29MB5-贝叶斯算法.pdf539.46KB4-聚类算法.pdf788.33KB3-决策树与集成算法.pdf1MB2-回归算法.pdf1.2MB1-AI入学指南.pdf658.64KB12-word2vec.pdf2.37MB11-神经网络.pdf11.7MB10-EM算法.pdf811.45KB部分代码资料
9-聚类算法实验分析
聚类算法-实验.zip1.71MBmldata
mnist-original.mat52.87MB8-Kmeans代码实现
Kmeans-代码实现.zip5.03MB7-聚类算法-Kmeans&Dbscan原理
4-聚类算法.pdf788.33KB6-逻辑回归实验分析
逻辑回归-实验.zip1.7MB5-逻辑回归代码实现
逻辑回归-代码实现.zip5.04MB3-线性回归实验分析
线性回归-实验.zip643.27KB3-模型评估方法
模型评估方法.ipynb91.18KBimg
9.png121.64KB8.png74.83KB7.png114.25KB6.png114.92KB5.png73.07KB4.png110.41KB3.png81.77KB2.png110.46KB1.png150.08KB2-线性回归代码实现
线性回归-代码实现.zip5.9MB1-线性回归原理推导
2-回归算法.pdf1.2MB15-支持向量机原理推导
6-支持向量机.pdf1.29MB14-集成算法实验分析
随机森林与集成算法-实验.zip11.88MBmldata
mnist-original.mat52.87MB13-集成算法原理
3-决策树与集成算法.pdf1MB12-决策树实验分析
决策树算法-实验.zip284.63KB11-决策树代码实现
决策树-代码实现.zip6.14KB10-决策树原理
3-决策树与集成算法.pdf1MB01.机器学习经典算法精讲视频课程
课程简介
Python机器学习实训营.docx11.29KB项目截图
QQ截图20190624141428.png140.94KBQQ截图20190624141330.png256.27KBQQ截图20190624141231.png103.51KBQQ截图20190624141129.png137.84KB1.png160.05KB第一章：线性回归原理推导
8-优化参数设置.mp426.8MB7参数更新方法.mp424.87MB6-梯度下降通俗解释.mp420.79MB5-参数求解.mp430.74MB4-似然函数的作用.mp429.04MB3-独立同分布的意义.mp424.48MB2-误差项定义.mp426.5MB1-回归问题概述.mp419.65MB0-课程简介.mp434.95MB第五章：逻辑回归原理推导
2-化简与求解.mp429.45MB1-逻辑回归算法原理.mp423MB第四章：线性回归实验分析
线性回归
1-实验目标分析.mp420.5MB9-多项式回归
9-多项式回归.mp437.56MB8-不同策略效果对比
8-不同策略效果对比.mp433.19MB7-MiniBatch方法
7-MiniBatch方法.mp431.23MB6-随机梯度下降得到的效果
6-随机梯度下降得到的效果.mp444.29MB5-学习率对结果的影响
5-学习率对结果的影响.mp432.27MB4-梯度下降模块
4-梯度下降模块.mp420.72MB3-预处理对结果的影响
3-预处理对结果的影响.mp454.85MB2-参数直接求解方法
2-参数直接求解方法.mp424.6MB14-实验总结
14-实验总结.mp456.17MB13-岭回归与lasso
13-岭回归与lasso.mp491.35MB12-正则化的作用
12-正则化的作用.mp433.82MB11-样本数量对结果的影响
11-样本数量对结果的影响.mp460.4MB10-模型复杂度
10-模型复杂度.mp464.95MB第十章：聚类算法实验分析
聚类
9-应用实例-图像分割
9-应用实例-图像分割_20190805_232021.mp439.45MB9-应用实例-图像分割.mp439.45MB8-Kmenas算法存在的问题
8-Kmenas算法存在的问题_20190805_232023.mp434.33MB8-Kmenas算法存在的问题.mp434.33MB7-轮廓系数的作用
7-轮廓系数的作用_20190805_232028.mp442.23MB7-轮廓系数的作用.mp442.23MB6-如何找到合适的K值
6-如何找到合适的K值_20190805_232026.mp434.7MB6-如何找到合适的K值.mp434.7MB5-评估指标-Inertia
5-评估指标-Inertia_20190805_232027.mp448.13MB5-评估指标-Inertia.mp448.13MB4-不稳定结果
4-不稳定结果_20190805_232028.mp418.31MB4-不稳定结果.mp418.31MB3-建模流程解读
3-建模流程解读_20190805_232032.mp449.18MB3-建模流程解读.mp449.18MB2-聚类结果展示
2-聚类结果展示_20190805_232030.mp419.58MB2-聚类结果展示.mp419.58MB1-Kmenas算法常用操作
1-Kmenas算法常用操作_20190805_232034.mp441.66MB1-Kmenas算法常用操作.mp441.66MB11-DBSCAN算法
11-DBSCAN算法_20190805_232033.mp455.48MB11-DBSCAN算法.mp455.48MB10-半监督学习
10-半监督学习_20190805_232033.mp447.43MB10-半监督学习.mp447.43MB第十一章：决策树原理
8-回归问题解决.mp418.27MB7-后剪枝方法.mp424.55MB6-预剪枝方法.mp425.09MB5-信息增益率与gini系数.mp418.2MB4-决策树构造实例.mp425.13MB3-信息增益原理.mp430.3MB2-熵的作用.mp422.82MB1-决策树算法概述.mp424.28MB第十三章：决策树实验分析
决策树
4-回归树模型
4-回归树模型.mp441.67MB3-树模型预剪枝参数作用
3-树模型预剪枝参数作用.mp442.67MB2-决策边界展示分析
2-决策边界展示分析.mp441.2MB1-树模型可视化展示
1-树模型可视化展示.mp430.7MB第十二章：决策树代码实现
第五章：决策树
7-测试算法效果
7-测试算法效果.mp422.75MB6-完成树模型构建
6-完成树模型构建.mp427.92MB5-数据集切分
5-数据集切分.mp427.5MB4-熵值计算
4-熵值计算.mp440.08MB3-整体框架逻辑
3-整体框架逻辑.mp420.41MB2-递归生成树节点
2-递归生成树节点.mp427.74MB1-整体模块概述
1-整体模块概述.mp411.68MB第三章：模型评估方法
分类模型评估
8-ROC曲线
8-ROC曲线.mp431.35MB7-阈值对结果的影响
7-阈值对结果的影响.mp444.63MB6-评估指标对比分析
6-评估指标对比分析.mp452.23MB5-混淆矩阵
5-混淆矩阵.mp423.79MB4-交叉验证实验分析
4-交叉验证实验分析.mp464.47MB3-交叉验证的作用
3-交叉验证的作用.mp447.01MB2-数据集切分
2-数据集切分.mp425.85MB1-Sklearn工具包简介
1-Sklearn工具包简介.mp436.64MB第七章：逻辑回归实验分析
6-多分类-softmax.mp460.57MB5-分类决策边界展示分析.mp461.13MB4-坐标棋盘制作.mp438.18MB3-可视化展示.mp433.21MB2-概率结果随特征数值的变化.mp446.69MB1-逻辑回归实验概述.mp452.15MB第六章：逻辑回归代码实现
第二章：逻辑回归
9-训练多分类模型
9-训练多分类模型.mp447.68MB8-鸢尾花数据集多分类任务
8-鸢尾花数据集多分类任务.mp427.41MB7-得出最终结果
7-得出最终结果.mp455.31MB6-梯度计算
6-梯度计算.mp448.52MB5-迭代优化参数
5-迭代优化参数.mp449.86MB4-优化目标定义
4-优化目标定义.mp437.97MB3-完成预测模块
3-完成预测模块.mp436.73MB2-训练模块功能
2-训练模块功能.mp442.8MB1-多分类逻辑回归整体思路
1-多分类逻辑回归整体思路.mp420.58MB12-非线性决策边界
12-非线性决策边界.mp422.6MB11-决策边界绘制
11-决策边界绘制.mp455.78MB10-准备测试数据
10-准备测试数据.mp440.83MB第九章：Kmeans代码实现
第三章：聚类-Kmeans
6-聚类效果展示
6-聚类效果展示.mp452.37MB5-鸢尾花数据集聚类任务
5-鸢尾花数据集聚类任务.mp432.25MB4-算法迭代更新
4-算法迭代更新.mp427.91MB3-样本点归属划分
3-样本点归属划分.mp425.85MB2-计算得到簇中心点
2-计算得到簇中心点.mp424.11MB1-Kmeans算法模块概述
Kmeans算法模块概述.mp49.91MB第二章：线性回归代码实现
第一章：线性回归
7-得到线性回归方程.mp435.82MB3-实现梯度下降优化模块.mp439.6MB2-初始化步骤.mp424.11MB1-线性回归整体模块概述.mp414.46MB9-多特征回归模型
9-多特征回归模型.mp462.24MB8-整体流程debug解读
8-整体流程debug解读.mp433.99MB6-训练线性回归模型
6-训练线性回归模型.mp444.68MB5-数据与标签定义
5-数据与标签定义.mp443.93MB4-损失与预测模块
4-损失与预测模块.mp446.72MB10-非线性回归
10-非线性回归.mp449.21MB第八章：聚类算法-Kmeans&Dbscan原理
6-DBSCAN可视化展示.mp432.97MB5-DBSCAN工作流程.mp441.61MB4-DBSCAN聚类算法.mp429.35MB3-KMEANS迭代可视化展示.mp431.7MB2-KMEANS工作流程.mp423.12MB1-KMEANS算法概述.mp428.94MB三：超详细人工智能学习大纲
人工智能大纲升级版本.pdf20.32MB六：计算机视觉实战项目
08.Unet图像分割课程资料
深度学习分割任务.pdf1.07MBunet++.zip409.6MB07.MASK-RCNN课程资料
第五章：迁移学习.zip91.92MB第四章：练手小项目-人体姿态识别demo.zip530.27MB第三章：基于MASK-RCNN框架训练自己的数据与任务.zip439.38MB第二章：MaskRcnn网络框架源码详解.zip1.14GB第六章：物体检测-faster-rcnn
iccv15_tutorial_training_rbg.pdf17.36MBFasterRcnn.zip2.74GB
faster-rcnn.pptx3.23MBFasterR-CNNTowardsReal-TimeObjectDetectionwithRegionProposalNetworks.pdf6.49MB06.YOLOV5目标检测课程资料
YOLO5.zip469.64MBYOLO.pdf1.88MBPyTorch-YOLOv3.zip462.21MBNEU-DET.zip26.68MB05.OpenCV图像处理课程资料
第十章：项目实战-文档扫描OCR识别.zip44.94MB第十五章：项目实战-答题卡识别判卷.zip3.07MB第十四章：项目实战-停车场车位识别.zip111.34MB第十三章：案例实战-全景图像拼接.zip829.49KB第十九章：项目实战-目标追踪.zip125.33MB第十八章：Opencv的DNN模块.zip49.62MB第九章：项目实战-信用卡数字识别.zip548.1KB第二十章：人脸关键点定位.zip69.75MB第二十一章：项目实战-疲劳检测.zip74.15MB第八章notebook课件.zip1.29MB第2-7章notebook课件.zip7.28MB第16-17章notebook课件.zip9.37MB第11-12章notebook课件.zip52.05MB04.Unet图像分割实战视频课程
5.mp4321.92MB4.mp4199.03MB3.mp4404.95MB2.mp4199.67MB1.mp4258.34MB03.MASK-RCNN目标检测实战视频课程
第一章：物体检测框架-MaskRcnn项目介绍与配置
第五章：必备基础-迁移学习与Resnet网络架构
8-迁移学习效果对比
8-迁移学习效果对比.mp453.12MB7-加载训练好的权重
7-加载训练好的权重.mp437.98MB6-shortcut模块
6-shortcut模块.mp440.55MB5-Resnet基本处理操作
5-Resnet基本处理操作.mp431.42MB4-Resnet网络细节
4-Resnet网络细节.mp438.85MB3-Resnet原理
3-Resnet原理.mp4107.27MB2-迁移学习策略
2-迁移学习策略.mp415.11MB1-迁移学习的目标
1-迁移学习的目标.mp411.47MB3-参数配置
0-参数配置.mp497.35MB2-开源项目数据集
0-开源项目数据集.mp442.25MB1-Mask-Rcnn开源项目简介
0-Mask-Rcnn开源项目简介.mp487.81MB0-课程简介
0-课程简介.mp418.57MB第四章：练手小项目-人体姿态识别demo
3-流程与结果演示
3-流程与结果演示.mp448.27MB2-网络架构概述
2-网络架构概述.mp432.38MB1-COCO数据集与人体姿态识别简介
1-COCO数据集与人体姿态识别简介.mp447.16MB第三章：基于MASK-RCNN框架训练自己的数据与任务
6-测试与展示模块
6-测试与展示模块.mp438.28MB5-基于标注数据训练所需任务
5-基于标注数据训练所需任务.mp439.36MB4-maskrcnn源码修改方法
4-maskrcnn源码修改方法.mp463.01MB3-完成训练数据准备工作
3-完成训练数据准备工作.mp426.13MB2-使用labelme进行数据与标签标注
2-使用labelme进行数据与标签标注.mp425.83MB1-Labelme工具安装
1-Labelme工具安装.mp414.08MB第六章：必备基础-物体检测FasterRcnn系列
7-论文解读-4-网络细节
论文解读-4-网络细节.mp4266.79MB6-论文解读-3-损失函数定义
论文解读-3-损失函数定义.mp4209.66MB5-论文解读-2-RPN网络结构
论文解读-2-RPN网络结构.mp4114.12MB4-论文解读-1-论文整体概述
论文解读-1.mp4121.72MB3-三代算法-3-faster-rcnn概述
三代算法-3-faster-rcnn概述.mp429.66MB2-三代算法-2-深度学习经典检测方法
三代算法-2-深度学习经典检测方法.mp439.13MB1-三代算法-1-物体检测概述
三代算法-1-物体检测概述.mp436.53MB第二章：MaskRcnn网络框架源码详解
9-正负样本选择与标签定义
9-正负样本选择与标签定义.mp427.31MB8-DetectionTarget层的作用
8-DetectionTarget层的作用.mp425.3MB7-Proposal层实现方法
7-Proposal层实现方法.mp432.9MB6-候选框过滤方法
6-候选框过滤方法.mp415.3MB5-RPN层的作用与实现解读
5-RPN层的作用与实现解读.mp430.41MB4-基于不同尺度特征图生成所有框
4-基于不同尺度特征图生成所有框.mp432.51MB3-生成框比例设置
3-生成框比例设置.mp427.86MB2-FPN网络架构实现解读
2-FPN网络架构实现解读.mp455.16MB1-FPN层特征提取原理解读
1-FPN层特征提取原理解读.mp441.63MB12-整体框架回顾
12-整体框架回顾.mp428.39MB11-RorAlign操作的效果
11-RorAlign操作的效果.mp425.33MB10-RoiPooling层的作用与目的
10-RoiPooling层的作用与目的.mp432.95MB02.YOLOV5目标检测视频课程
7-输出结果与项目总结.mp432.44MB6-缺陷检测模型培训.mp427.21MB5-项目参数配置.mp418.87MB4-各版本模型介绍.mp424.31MB3-标签转格式脚本制作.mp423.84MB2-数据与标签配置方法.mp428.38MB1.任务需求与项目概述.mp412.45MB01.OpenCV图像处理实战视频课程
项目实战一：信用卡数字识别
5-模板匹配得出识别结果
5-模板匹配得出识别结果.mp447.17MB4-输入数据处理方法
4-输入数据处理方法.mp428.43MB3-模板处理方法
3-模板处理方法.mp423.33MB2-环境配置与预处理
2-环境配置与预处理.mp434.42MB1-总体流程与方法讲解
总体流程与方法讲解.mp420.27MB项目实战五：答题卡识别判卷
4-选项判断识别
4-选项判断识别.mp456.6MB3-填涂轮廓检测
3-填涂轮廓检测.mp425.27MB2-预处理操作
2-预处理操作.mp423.72MB1-整体流程与效果概述
1-整体流程与效果概述.mp429.14MB项目实战四：停车场车位识别
8-基于视频的车位检测
8-基于视频的车位检测.mp4135.13MB7-识别模型构建
7-识别模型构建.mp440.85MB6-车位区域划分
6-车位区域划分.mp456.77MB5-按列划分区域
5-按列划分区域.mp454.11MB4-车位直线检测
4-车位直线检测.mp460.83MB3-图像数据预处理
3-图像数据预处理.mp456.29MB2-所需数据介绍
2-所需数据介绍.mp434.02MB1-任务整体流程
1-任务整体流程.mp471.02MB项目实战三：全景图像拼接
4-流程解读
4-流程解读.mp421.39MB2-图像拼接方法
2-图像拼接方法.mp444.55MB2-RANSAC算法
2-RANSAC算法.mp434.01MB1-特征匹配方法
1-特征匹配方法.mp428.13MB项目实战二：文档扫描OCR识别
6-文档扫描识别效果
6-文档扫描识别效果.mp428.59MB5-tesseract-ocr安装配置
5-tesseract-ocr安装配置.mp440.87MB4-透视变换结果
4-透视变换结果.mp432.43MB3-原始与变换坐标计算
3-原始与变换坐标计算.mp425.84MB2-文档轮廓提取
2-文档轮廓提取.mp427.37MB1-整体流程演示
1-整体流程演示.mp421.22MB二：AI必读经典书籍
02.AI必读经典书籍
OpenCV书籍.rar63.15MB04.计算机视觉相关书籍
超详细的计算机视觉书籍.zip1.03GB03.深度学习相关书籍
深度学习技术图像处理入门by杨培文，胡博强().pdf125.1MB深度学习(花园书).pdf32.99MBTensorflow技术解析与实战.pdf39.49MB《神经网络与深度学习》(邱锡鹏-20191121).pdf7.02MB《TensorFlow2.0深度学习算法实战教材》-中文版教材分享.pdf21.41MB21年最新-李沐《动手学深度学习第二版》中、英文版免费分享
Dive-into-DL-Pytorch.pdf33.5MBd2l-zh-pytorch.pdf18.1MBd2l-en-pytorch.pdf26.97MB《深度学习之PyTorch物体检测实战》PDF+源代码
深度学习之PyTorch物体检测实战论文导引.docx30.41KB深度学习之PyTorch物体检测实战.pdf11.64MB
深度学习之PyTorch物体检测实战.mobi12.71MB
深度学习之PyTorch物体检测实战.epub10.35MB源代码
GitHub地址.txt57ByteDetection-PyTorch-Notebook
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