教程之家 - 37.4GB AI精选付费资料包：从入门到实战

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[2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.jpg29.73KB

Survey

一般推荐

NeuralMessagePassingforQuantumChemistry.pdf511.15KB

GeometricDeepLearning-GoingbeyondEuclideandata.pdf5.26MB

DeepLearningonGraphs-ASurvey.pdf1.8MB

ComputationalCapabilitiesofGraphNeuralNetworks(1).pdf1.28MB

AComprehensiveSurveyonGraphNeuralNetworks.pdf1.8MB

极力推荐

TheGraphNeuralNetworkModel.pdf1.43MB

RelationalInductiveBiases,DeepLearning,andGraphNetworks.pdf8.99MB

Non-localNeuralNetworks.pdf1.24MB

GraphNeuralNetworks：AReviewofMethodsandApplications.pdf2.67MB

Models

trainingmethods

Neuralnetworksforrelationallearning-anexperimentalcomparison.pdf1.15MB

LearningSteady-StatesofIterativeAlgorithmsoverGraphs.pdf3.09MB

Knowledge-GuidedRecurrentNeuralNetworkLearningforTask-OrientedActionPrediction.pdf1000.46KB

HierarchicalGraphRepresentationLearningwithDifferentiablePooling.pdf2.31MB

Graphical-BasedLearningEnvironmentsforPatternRecognition.pdf335.92KB

CovariantCompositionalNetworksForLearningGraphs.pdf482.53KB

receptivefieldcontrol

StochasticTrainingofGraphConvolutionalNetworkswithVarianceReduction.pdf1.25MB

neighborhoodsampling

InductiveRepresentationLearningonLargeGraphs.pdf1.04MB

FastGCN-FastLearningwithGraphConvolutionalNetworksviaImportanceSampling.pdf358.35KB

AdaptiveSamplingTowardsFastGraphRepresentationLearning.pdf579.95KB

boosting

DeeperInsightsintoGraphConvolutionalNetworksforSemi-SupervisedLearning.pdf1.96MB

propagation_type

skip

Semi-SupervisedClassificationwithGraphConvolutionalNetworks.pdf853.42KB

RepresentationLearningonGraphswithJumpingKnowledgeNetworks.pdf3.15MB

gate

Sentence-StateLSTMforTextRepresentation.pdf442.27KB

GatedGraphSequenceNeuralNetworks.pdf748.16KB

convolution

Structure-AwareConvolutionalNeuralNetworks.pdf1.36MB

SpectralNetworksandDeepLocallyConnected.pdf1.86MB

LearningConvolutionalNeuralNetworksforGraphs.pdf639.85KB

DeepConvolutionalNetworksonGraph-StructuredData.pdf4.57MB

ConvolutionalNeuralNetworksonGraphswithFastLocalizedSpectralFiltering.pdf459.44KB

BayesianSemi-supervisedLearningwithGraphGaussianProcesses.pdf689.89KB

attention

GraphClassificationusingStructuralAttention.pdf2.47MB

GraphAttentionNetworks.pdf1.48MB

AttentionIsAllYouNeed.pdf2.1MB

others

Geometricdeeplearningongraphsandmanifoldsusingmixturemodelcnns.pdf7.23MB

Diffusion-ConvolutionalNeuralNetworks.pdf366.35KB

DerivingNeuralArchitecturesfromSequenceandGraphKernels.pdf687.05KB

DeepSets.pdf5.11MB

ContextualGraphMarkovModel-ADeepandGenerativeApproachtoGraphProcessing.pdf570.59KB

CelebrityNet-ASocialNetworkConstructedfromLarge-ScaleOnlineCelebrityImages.pdf16.33MB

Anewmodelforlearningingraphdomains.pdf177.61KB

AComparisonbetweenRecursiveNeuralNetworksandGraphNeuralNetworks.pdf247.2KB

graph_type

Mean-fieldtheoryofgraphneuralnetworksingraphpartitioning.pdf369.44KB

HowPowerfulareGraphNeuralNetworks-.pdf678.3KB

GraphPartitionNeuralNetworksforSemi-SupervisedClassification.pdf713.9KB

GraphNeuralNetworksforRankingWebPages.pdf1.01MB

GraphNeuralNetworksforObjectLocalization.pdf221.83KB

GraphCapsuleConvolutionalNeuralNetworks.pdf1.93MB

AdaptiveGraphConvolutionalNeuralNetworks.pdf803.92KB

edge-informativegraph

Modelingrelationaldatawithgraphconvolutionalnetworks.pdf323.62KB

Graph-to-SequenceLearningusingGatedGraphNeuralNetworks.pdf4.06MB

directedgraph

RethinkingKnowledgeGraphPropagationforZero-ShotLearning.pdf4.21MB

Applications

text

RecurrentRelationalNetworks.pdf307KB

N-aryrelationextractionusinggraphstateLSTM.pdf455.67KB

JointlyMultipleEventsExtractionviaAttention-basedGraph.pdf430.38KB

GraphConvolutionalNetworkswithArgument-AwarePoolingforEventDetection.pdf324.7KB

GraphConvolutionalNetworksforTextClassification.pdf1.83MB

GraphConvolutionalEncodersforSyntax-awareNeuralMachineTranslation.pdf346.9KB

GraphConvolutionoverPrunedDependencyTreesImprovesRelationExtraction.pdf784.41KB

ExploringGraph-structuredPassageRepresentationforMulti-hopReadingComprehensionwithGraphNeuralNetworks..pdf453.5KB

ExploitingSemanticsinNeuralMachineTranslationwithGraphConvolutionalNetworks.pdf604.59KB

End-to-EndRelationExtractionusingLSTMsonSequencesandTreeStructures.pdf363.06KB

EncodingSentenceswithGraphConvolutionalNetworksforSemanticRoleLabeling.pdf621.87KB

AGraph-to-SequenceModelforAMR-to-TextGeneration.pdf290.15KB

science

VisualInteractionNetworks-LearningaPhysicsSimulatorfromVide.o.pdf5.41MB

VAIN-AttentionalMulti-agentPredictiveModeling.pdf423.97KB

UnderstandingKinRelationshipsinaPhoto.pdf1.44MB

TranslatingEmbeddingsforModelingMulti-relationalData.pdf414.17KB

TrafficGraphConvolutionalRecurrentNeuralNetwork-ADeepLearningFrameworkforNetwork-ScaleTrafficLearningandForecasting.pdf1.45MB

SymbolicGraphReasoningMeetsConvolutions.pdf3.23MB

StructuredDialoguePolicywithGraphNeuralNetworks.pdf779.24KB

Spatio-TemporalGraphConvolutionalNetworks-ADeepLearningFrameworkforTrafficForecasting.pdf895.05KB

SpatialTemporalGraphConvolutionalNetworksforSkeleton-BasedActionRecognition.pdf1.5MB

SituationRecognitionwithGraphNeuralNetworks.pdf5.27MB

Semi-supervisedUserGeolocationviaGraphConvolutionalNetworks.pdf1.13MB

Self-AttentionwithRelativePositionRepresentations.pdf229.86KB

Relationalneuralexpectationmaximization-Unsuperviseddiscoveryofobjectsandtheirinteractions.pdf1.15MB

Relationalinductivebiasforphysicalconstructioninhumansandmachines.pdf1022.51KB

RelationalDeepReinforcementLearning.pdf6.81MB

ProteinInterfacePredictionusingGraphConvolutionalNetworks.pdf837.75KB

NeuralRelationalInferenceforInteractingSystems.pdf2.83MB

NeuralModuleNetworks.pdf1.03MB

NeuralCombinatorialOptimizationwithReinforcementLearning.pdf393.17KB

NerveNetLearningStructuredPolicywithGraphNeuralNetworks.pdf3.11MB

MolecularGraphConvolutions-MovingBeyondFingerprints.pdf2.08MB

MetacontrolforAdaptiveImagination-BasedOptimization.pdf1.6MB

LearningtoRepresentProgramswithGraphs.pdf421.9KB

LearningMultiagentCommunicationwithBackpropagation.pdf4.13MB

Learningmodel-basedplanningfromscratch.pdf1.28MB

LearningHuman-ObjectInteractionsbyGraphParsingNeuralNetworks.pdf3.91MB

LearningGraphicalStateTransitions.pdf1.47MB

LearningDeepGenerativeModelsofGraphs.pdf2.31MB

LearningConditionedGraphStructuresforInterpretableVisualQuestionAnswering.pdf8.48MB

LearningaSATSolverfromSingle-BitSupervision.pdf1.89MB

InteractionNetworksforLearningaboutObjects,RelationsandPhysics.pdf1.91MB

InferenceinProbabilisticGraphicalModelsbyGraphNeuralNetworks.pdf3.07MB

ImprovedSemanticRepresentationsFromTree-StructuredLongShort-TermMemoryNetworks.pdf304.16KB

HyperbolicAttentionNetworks.pdf3.08MB

HybridApproachofRelationNetworkandLocalizedGraphConvolutionalFilteringforBreastCancerSubtypeClassification.pdf2.52MB

GraphRNN-GeneratingRealisticGraphswithDeepAuto-regressiveModels.pdf2.43MB

Graphnetworksaslearnablephysicsenginesforinferenceandcontrol.pdf2.72MB

GraphConvolutionalNeuralNetworksforWeb-ScaleRecommenderSystems.pdf9.84MB

GraphConvolutionalMatrixCompletion.pdf732.99KB

GeometricMatrixCompletionwithRecurrentMulti-GraphNeuralNetworks.pdf6.99MB

EffectiveApproachestoAttention-basedNeuralMachineTranslation.pdf243.97KB

DynamicEdge-ConditionedFiltersinConvolutionalNeuralNetworksonGraphs.pdf567.07KB

Discoveringobjectsandtheirrelationsfromentangledscenerepresentations.pdf4.99MB

DeepInf-Modelinginfluencelocalityinlargesocialnetworks.pdf1.07MB

DeepGraphInfomax.pdf8.15MB

Cross-SentenceN-aryRelationExtractionwithGraphLSTMs.pdf540.89KB

Convolutionalnetworksongraphsforlearningmolecularfingerprints.pdf785.36KB

ConversationModelingonRedditusingaGraph-StructuredLSTM.pdf682.35KB

ConstructingNarrativeEventEvolutionaryGraphforScriptEventPrediction.pdf654.87KB

ConstrainedGenerationofSemanticallyValidGraphsviaRegularizingVariationalAutoencoders.pdf567.14KB

CombiningNeuralNetworkswithPersonalizedPageRankforClassificationonGraphs.pdf483.25KB

BeyondCategories-TheVisualMemexModelforReasoningAboutObjectRelationships.pdf618.71KB

Attention,LearntoSolveRoutingProblems!.pdf1.48MB

Attend,Infer,Repeat-FastSceneUnderstandingwithGenerativeModels.pdf1.3MB

AdversarialAttackonGraphStructuredData.pdf593.12KB

ActionSchemaNetworks-GeneralisedPolicieswithDeepLearning.pdf1.67MB

Asimpleneuralnetworkmoduleforrelationalreasoning.pdf1.37MB

ANoteonLearningAlgorithmsforQuadraticAssignmentwithGraphNeuralNetworks.pdf340.4KB

ACompositionalObject-BasedApproachtoLearningPhysicalDynamics.pdf4.26MB

knowledgegraph

Zero-shotRecognitionviaSemanticEmbeddingsandKnowledgeGraphs.pdf1.63MB

TheMoreYouKnow-UsingKnowledgeGraphsforImageClassification.pdf2.31MB

Representationlearningforvisual-relationalknowledgegraphs.pdf6.9MB

Multi-LabelZero-ShotLearningwithStructuredKnowledgeGraphs.pdf1.36MB

ModelingSemanticswithGatedGraphNeuralNetworksforKnowledgeBaseQuestionAnswering.pdf437.8KB

KnowledgeTransferforOut-of-Knowledge-BaseEntities-AGraphNeuralNetworkApproach.pdf355.22KB

DynamicGraphGenerationNetwork-GeneratingRelationalKnowledgefromDiagrams.pdf1.19MB

DeepReasoningwithKnowledgeGraphforSocialRelationshipUnderstanding.pdf2.76MB

Cross-lingualKnowledgeGraphAlignmentviaGraphConvolutionalNetworks.pdf432.63KB

image

VisualQuestionAnswering

OutoftheBox-ReasoningwithGraphConvolutionNetsforFactualVisualQuestionAnswering(1).pdf2.45MB

Graph-StructuredRepresentationsforVisualQuestionAnswering.pdf3.74MB

SemanticSegmentation

PointNet-DeepLearningonPointSetsfor3DClassificationandSegmentation.pdf8.66MB

Modelingpolypharmacysideeffectswithgraphconvolutionalnetworks.pdf4.18MB

Large-scalePointCloudSemanticSegmentationwithSuperpointGraphs.pdf4.83MB

DynamicGraphCNNforLearningonPointClouds.pdf5.07MB

3DGraphNeuralNetworksforRGBDSemanticSegmentation.pdf2.23MB

RegionClassification

IterativeVisualReasoningBeyondConvolutions..pdf3.91MB

ObjectDetection

RelationNetworksforObjectDetection.pdf906.66KB

LearningRegionfeaturesforObjectDetection.pdf1.68MB

InteractionDetection

Structural-RNN-DeepLearningonSpatio-TemporalGraphs.pdf1.1MB

Imageclassification

Few-ShotLearningwithGraphNeuralNetworks.pdf1.69MB

graphgeneration

NetGAN-GeneratingGraphsviaRandomWalks(1).pdf1.67MB

MolGAN-Animplicitgenerativemodelforsmallmoleculargraphs(1).pdf1.1MB

GraphConvolutionalPolicyNetworkforGoal-DirectedMolecularGraphGeneration.pdf517.97KB

combinatorialoptimization

LearningCombinatorialOptimizationAlgorithmsoverGraphs.pdf2.91MB

CombinatorialOptimizationwithGraphConvolutionalNetworksandGuidedTreeSearch(1).pdf537.04KB

深度学习论文精讲-BERT模型

9.8-论文总结分析.mp476.41MB

8.7-BERT模型训练策略.mp442.95MB

7.6-向量特征编码方法.mp424.65MB

6.5-输入数据特殊编码字符解析.mp443.88MB

5.4-预训练模型的作用.mp418.43MB

4.3-模型在NLP领域应用效果.mp433.56MB

3.2-BERT模型摘要概述.mp432.28MB

2.1-论文讲解思路概述.mp414.77MB

1.课程介绍.mp436.9MB

Resnet论文解读

13-额外补充-Resnet论文解读.mp4117.38MB

ICCV2021

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.mp423.49MB

5.4-基于图卷积构建人体拓扑关系.mp425.86MB

4.3-局部特征热度图计算.mp421.11MB

2.2-图卷积与匹配的作用.mp420.81MB

1.1-关键点位置特征构建.mp417.96MB

cvpr2021

解压密码：cvpr2021

CNN_不能错过的10篇论文

Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf1.24MB

4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf1.35MB

1512.03385v1_DeepResidualLearningforImageRecognition.pdf800.18KB

1506.02025_SpatialTransformerNetworks.pdf7.89MB

1506.01497v3_FasterR-CNN.pdf6.59MB

1504.08083_FastR-CNN.pdf713.99KB

1412.2306v2_DeepVisual-SemanticAlignmentsforGeneratingImageDescriptions.pdf5.21MB

1409.1556v6_VERYDEEPCONVOLUTIONALNetworks.pdf195.32KB

1406.2661v1_GenerativeAdversarialNets.pdf518.05KB

1311.2901v3_VisualizingandUnderstandingConvolutionalNetworks.pdf34.56MB

1311.2524v5_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.zip80.87MB

CNN+RNN+GAN

源代码和PPT在Github下载.txt72Byte

课程安装软件-Win10

pycharm-community-2019.1.1.exe231.79MB

cudnn-10.0-windows10-x64-v7.5.0.56(1).zip213.78MB

cuda_10.0.130_411.31_win10.exe2.04GB

Anaconda3-2019.03-Windows-x86_64.exe661.66MB

课程安装软件-Ubuntu18.04

cudnn-10.0-linux-x64-v7.5.0.56.tgz412.76MB

cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb1.55GB

Anaconda3-2019.03-Linux-x86_64.sh654.13MB

RNN循环神经网络基础

9.课时9LSTM中Layer的使用.mp412.17MB

8.课时8LSTM基本原理-2.mp416.19MB

7.课时7LSTM基本原理-1.mp412.95MB

6.课时6项目实战-时间序列预测问题.mp419.59MB

5.课时5循环神经网络中Layer的使用-2.mp414.44MB

4.课时4循环神经网络中Layer使用-1.mp416.04MB

3.课时3循环神经网络基本原理-2.mp416.3MB

2.课时2循环神经网络基本原理-1.mp413.68MB

11.课时11项目实战-情感分类问题.mp433.45MB

10.课时10RNN训练难题—梯度弥散与梯度爆炸.mp423.86MB

1.课时1时间序列介绍.mp421.59MB

GAN对抗生成网络基础

9GAN实战-1.flv16.82MB

8WGAN-GP原理.flv30.63MB

7EM距离.flv19.16MB

6GAN训练难题.flv37.04MB

5纳什均衡-2.flv35.39MB

4纳什均衡-1.flv19.17MB

3生成对抗网络.flv25.78MB

2画家的成长历程.flv29.46MB

12WGAN实战-2.flv35.24MB

11WGAN实战-1.flv19.71MB

10GAN实战-2.flv33.53MB

1数据的分布.flv17.67MB

CNN卷积神经网络基础

9-池化与采样操作讲解.mp417.02MB

8-卷积神经网络图解-4.mp426.54MB

7-卷积神经网络图解-3.mp425.8MB

6-卷积神经网络图解-2.mp430.4MB

5-卷积神经网络图解-1.mp436.88MB

4-卷积运算详解-4.mp421.91MB

3-卷积运算详解-3.mp428.37MB

2-卷积运算详解-2.mp427.98MB

23-ResNet实战-4.mp417.54MB

22-ResNet实战-3.mp415.11MB

21-ResNet实战-2.mp414.52MB

20-ResNet实战-1.mp414.3MB

1-卷积运算详解-1.mp430.81MB

19-ResNet,DenseNet详解.mp418.99MB

18-ResNet,DenseNet详解.mp418.06MB

17-BatchNorm-2.mp430.27MB

15-经典卷积神经网络详解-2.mp413.17MB

14-经典卷积神经网络详解-1.mp416.68MB

13-CIFAR100与VGG13实战-4.mp410.4MB

12-CIFAR100与VGG13实战-3.mp414.83MB

11-CIFAR100与VGG13实战-2.mp413.69MB

10-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.pdf767.26KB

9-LDA与PCA算法.pdf1.04MB

8-xgboost.pdf932.12KB

7-推荐系统.pdf1.97MB

6-支持向量机.pdf1.29MB

5-贝叶斯算法.pdf539.46KB

4-聚类算法.pdf788.33KB

3-决策树与集成算法.pdf1MB

2-回归算法.pdf1.2MB

1-AI入学指南.pdf658.64KB

12-word2vec.pdf2.37MB

11-神经网络.pdf11.7MB

10-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.zip1.71MB

mldata

mnist-original.mat52.87MB

8-Kmeans代码实现

Kmeans-代码实现.zip5.03MB

7-聚类算法-Kmeans&Dbscan原理

4-聚类算法.pdf788.33KB

6-逻辑回归实验分析

逻辑回归-实验.zip1.7MB

5-逻辑回归代码实现

逻辑回归-代码实现.zip5.04MB

3-线性回归实验分析

线性回归-实验.zip643.27KB

3-模型评估方法

模型评估方法.ipynb91.18KB

img

9.png121.64KB

8.png74.83KB

7.png114.25KB

6.png114.92KB

5.png73.07KB

4.png110.41KB

3.png81.77KB

2.png110.46KB

1.png150.08KB

2-线性回归代码实现

线性回归-代码实现.zip5.9MB

1-线性回归原理推导

2-回归算法.pdf1.2MB

15-支持向量机原理推导

6-支持向量机.pdf1.29MB

14-集成算法实验分析

随机森林与集成算法-实验.zip11.88MB

mldata

mnist-original.mat52.87MB

13-集成算法原理

3-决策树与集成算法.pdf1MB

12-决策树实验分析

决策树算法-实验.zip284.63KB

11-决策树代码实现

决策树-代码实现.zip6.14KB

10-决策树原理

3-决策树与集成算法.pdf1MB

01.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.png140.94KB

QQ截图20190624141330.png256.27KB

QQ截图20190624141231.png103.51KB

QQ截图20190624141129.png137.84KB

1.png160.05KB

第一章：线性回归原理推导

8-优化参数设置.mp426.8MB

7参数更新方法.mp424.87MB

6-梯度下降通俗解释.mp420.79MB

5-参数求解.mp430.74MB

4-似然函数的作用.mp429.04MB

3-独立同分布的意义.mp424.48MB

2-误差项定义.mp426.5MB

1-回归问题概述.mp419.65MB

0-课程简介.mp434.95MB

第五章：逻辑回归原理推导

2-化简与求解.mp429.45MB

1-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.mp420.5MB

9-多项式回归

9-多项式回归.mp437.56MB

8-不同策略效果对比

8-不同策略效果对比.mp433.19MB

7-MiniBatch方法

7-MiniBatch方法.mp431.23MB

6-随机梯度下降得到的效果

6-随机梯度下降得到的效果.mp444.29MB

5-学习率对结果的影响

5-学习率对结果的影响.mp432.27MB

4-梯度下降模块

4-梯度下降模块.mp420.72MB

3-预处理对结果的影响

3-预处理对结果的影响.mp454.85MB

2-参数直接求解方法

2-参数直接求解方法.mp424.6MB

14-实验总结

14-实验总结.mp456.17MB

13-岭回归与lasso

13-岭回归与lasso.mp491.35MB

12-正则化的作用

12-正则化的作用.mp433.82MB

11-样本数量对结果的影响

11-样本数量对结果的影响.mp460.4MB

10-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.mp439.45MB

9-应用实例-图像分割.mp439.45MB

8-Kmenas算法存在的问题

8-Kmenas算法存在的问题_20190805_232023.mp434.33MB

8-Kmenas算法存在的问题.mp434.33MB

7-轮廓系数的作用

7-轮廓系数的作用_20190805_232028.mp442.23MB

7-轮廓系数的作用.mp442.23MB

6-如何找到合适的K值

6-如何找到合适的K值_20190805_232026.mp434.7MB

6-如何找到合适的K值.mp434.7MB

5-评估指标-Inertia

5-评估指标-Inertia_20190805_232027.mp448.13MB

5-评估指标-Inertia.mp448.13MB

4-不稳定结果

4-不稳定结果_20190805_232028.mp418.31MB

4-不稳定结果.mp418.31MB

3-建模流程解读

3-建模流程解读_20190805_232032.mp449.18MB

3-建模流程解读.mp449.18MB

2-聚类结果展示

2-聚类结果展示_20190805_232030.mp419.58MB

2-聚类结果展示.mp419.58MB

1-Kmenas算法常用操作

1-Kmenas算法常用操作_20190805_232034.mp441.66MB

1-Kmenas算法常用操作.mp441.66MB

11-DBSCAN算法

11-DBSCAN算法_20190805_232033.mp455.48MB

11-DBSCAN算法.mp455.48MB

10-半监督学习

10-半监督学习_20190805_232033.mp447.43MB

10-半监督学习.mp447.43MB

第十一章：决策树原理

8-回归问题解决.mp418.27MB

7-后剪枝方法.mp424.55MB

6-预剪枝方法.mp425.09MB

5-信息增益率与gini系数.mp418.2MB

4-决策树构造实例.mp425.13MB

3-信息增益原理.mp430.3MB

2-熵的作用.mp422.82MB

1-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.mp441.67MB

3-树模型预剪枝参数作用

3-树模型预剪枝参数作用.mp442.67MB

2-决策边界展示分析

2-决策边界展示分析.mp441.2MB

1-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.mp422.75MB

6-完成树模型构建

6-完成树模型构建.mp427.92MB

5-数据集切分

5-数据集切分.mp427.5MB

4-熵值计算

4-熵值计算.mp440.08MB

3-整体框架逻辑

3-整体框架逻辑.mp420.41MB

2-递归生成树节点

2-递归生成树节点.mp427.74MB

1-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.mp431.35MB

7-阈值对结果的影响

7-阈值对结果的影响.mp444.63MB

6-评估指标对比分析

6-评估指标对比分析.mp452.23MB

5-混淆矩阵

5-混淆矩阵.mp423.79MB

4-交叉验证实验分析

4-交叉验证实验分析.mp464.47MB

3-交叉验证的作用

3-交叉验证的作用.mp447.01MB

2-数据集切分

2-数据集切分.mp425.85MB

1-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.mp460.57MB

5-分类决策边界展示分析.mp461.13MB

4-坐标棋盘制作.mp438.18MB

3-可视化展示.mp433.21MB

2-概率结果随特征数值的变化.mp446.69MB

1-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.mp447.68MB

8-鸢尾花数据集多分类任务

8-鸢尾花数据集多分类任务.mp427.41MB

7-得出最终结果

7-得出最终结果.mp455.31MB

6-梯度计算

6-梯度计算.mp448.52MB

5-迭代优化参数

5-迭代优化参数.mp449.86MB

4-优化目标定义

4-优化目标定义.mp437.97MB

3-完成预测模块

3-完成预测模块.mp436.73MB

2-训练模块功能

2-训练模块功能.mp442.8MB

1-多分类逻辑回归整体思路

1-多分类逻辑回归整体思路.mp420.58MB

12-非线性决策边界

12-非线性决策边界.mp422.6MB

11-决策边界绘制

11-决策边界绘制.mp455.78MB

10-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.mp452.37MB

5-鸢尾花数据集聚类任务

5-鸢尾花数据集聚类任务.mp432.25MB

4-算法迭代更新

4-算法迭代更新.mp427.91MB

3-样本点归属划分

3-样本点归属划分.mp425.85MB

2-计算得到簇中心点

2-计算得到簇中心点.mp424.11MB

1-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.mp435.82MB

3-实现梯度下降优化模块.mp439.6MB

2-初始化步骤.mp424.11MB

1-线性回归整体模块概述.mp414.46MB

9-多特征回归模型

9-多特征回归模型.mp462.24MB

8-整体流程debug解读

8-整体流程debug解读.mp433.99MB

6-训练线性回归模型

6-训练线性回归模型.mp444.68MB

5-数据与标签定义

5-数据与标签定义.mp443.93MB

4-损失与预测模块

4-损失与预测模块.mp446.72MB

10-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.mp432.97MB

5-DBSCAN工作流程.mp441.61MB

4-DBSCAN聚类算法.mp429.35MB

3-KMEANS迭代可视化展示.mp431.7MB

2-KMEANS工作流程.mp423.12MB

1-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.pdf1.07MB

unet++.zip409.6MB

07.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.pdf17.36MB

FasterRcnn.zip2.74GB

faster-rcnn.pptx3.23MB

FasterR-CNNTowardsReal-TimeObjectDetectionwithRegionProposalNetworks.pdf6.49MB

06.YOLOV5目标检测课程资料

YOLO5.zip469.64MB

YOLO.pdf1.88MB

PyTorch-YOLOv3.zip462.21MB

NEU-DET.zip26.68MB

05.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.zip52.05MB

04.Unet图像分割实战视频课程

5.mp4321.92MB

4.mp4199.03MB

3.mp4404.95MB

2.mp4199.67MB

1.mp4258.34MB

03.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.mp453.12MB

7-加载训练好的权重

7-加载训练好的权重.mp437.98MB

6-shortcut模块

6-shortcut模块.mp440.55MB

5-Resnet基本处理操作

5-Resnet基本处理操作.mp431.42MB

4-Resnet网络细节

4-Resnet网络细节.mp438.85MB

3-Resnet原理

3-Resnet原理.mp4107.27MB

2-迁移学习策略

2-迁移学习策略.mp415.11MB

1-迁移学习的目标

1-迁移学习的目标.mp411.47MB

3-参数配置

0-参数配置.mp497.35MB

2-开源项目数据集

0-开源项目数据集.mp442.25MB

1-Mask-Rcnn开源项目简介

0-Mask-Rcnn开源项目简介.mp487.81MB

0-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.mp448.27MB

2-网络架构概述

2-网络架构概述.mp432.38MB

1-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.mp438.28MB

5-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.mp439.36MB

4-maskrcnn源码修改方法

4-maskrcnn源码修改方法.mp463.01MB

3-完成训练数据准备工作

3-完成训练数据准备工作.mp426.13MB

2-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.mp425.83MB

1-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.mp4266.79MB

6-论文解读-3-损失函数定义

论文解读-3-损失函数定义.mp4209.66MB

5-论文解读-2-RPN网络结构

论文解读-2-RPN网络结构.mp4114.12MB

4-论文解读-1-论文整体概述

论文解读-1.mp4121.72MB

3-三代算法-3-faster-rcnn概述

三代算法-3-faster-rcnn概述.mp429.66MB

2-三代算法-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.mp439.13MB

1-三代算法-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.mp427.31MB

8-DetectionTarget层的作用

8-DetectionTarget层的作用.mp425.3MB

7-Proposal层实现方法

7-Proposal层实现方法.mp432.9MB

6-候选框过滤方法

6-候选框过滤方法.mp415.3MB

5-RPN层的作用与实现解读

5-RPN层的作用与实现解读.mp430.41MB

4-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.mp432.51MB

3-生成框比例设置

3-生成框比例设置.mp427.86MB

2-FPN网络架构实现解读

2-FPN网络架构实现解读.mp455.16MB

1-FPN层特征提取原理解读

1-FPN层特征提取原理解读.mp441.63MB

12-整体框架回顾

12-整体框架回顾.mp428.39MB

11-RorAlign操作的效果

11-RorAlign操作的效果.mp425.33MB

10-RoiPooling层的作用与目的

10-RoiPooling层的作用与目的.mp432.95MB

02.YOLOV5目标检测视频课程

7-输出结果与项目总结.mp432.44MB

6-缺陷检测模型培训.mp427.21MB

5-项目参数配置.mp418.87MB

4-各版本模型介绍.mp424.31MB

3-标签转格式脚本制作.mp423.84MB

2-数据与标签配置方法.mp428.38MB

1.任务需求与项目概述.mp412.45MB

01.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.mp447.17MB

4-输入数据处理方法

4-输入数据处理方法.mp428.43MB

3-模板处理方法

3-模板处理方法.mp423.33MB

2-环境配置与预处理

2-环境配置与预处理.mp434.42MB

1-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.mp456.6MB

3-填涂轮廓检测

3-填涂轮廓检测.mp425.27MB

2-预处理操作

2-预处理操作.mp423.72MB

1-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.mp4135.13MB

7-识别模型构建

7-识别模型构建.mp440.85MB

6-车位区域划分

6-车位区域划分.mp456.77MB

5-按列划分区域

5-按列划分区域.mp454.11MB

4-车位直线检测

4-车位直线检测.mp460.83MB

3-图像数据预处理

3-图像数据预处理.mp456.29MB

2-所需数据介绍

2-所需数据介绍.mp434.02MB

1-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.mp421.39MB

2-图像拼接方法

2-图像拼接方法.mp444.55MB

2-RANSAC算法

2-RANSAC算法.mp434.01MB

1-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.mp428.59MB

5-tesseract-ocr安装配置

5-tesseract-ocr安装配置.mp440.87MB

4-透视变换结果

4-透视变换结果.mp432.43MB

3-原始与变换坐标计算

3-原始与变换坐标计算.mp425.84MB

2-文档轮廓提取

2-文档轮廓提取.mp427.37MB

1-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.rar63.15MB

04.计算机视觉相关书籍

超详细的计算机视觉书籍.zip1.03GB

03.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).pdf32.99MB

Tensorflow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.pdf21.41MB

21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.pdf33.5MB

d2l-zh-pytorch.pdf18.1MB

d2l-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.txt57Byte

Detection-PyTorch-Notebook

README.md29Byte

chapter8

retinanet.py1.3KB

nms.py895Byte

chapter7

squeezenet_fire.py978Byte

shufflenet_v1.py1.94KB

mobilenet_v2_block.py743Byte

mobilenet_v2.py4.11KB

mobilenet_v1.py1.45KB

chapter6

yolov2-pytorch

train.py4.7KB

test.py4.93KB

requirements.txt64Byte

README.md4.63KB

make.sh488Byte

demo.py2.73KB

darknet.py12.01KB

utils

yolo.pyx1.69KB

yolo.py7.31KB

yolo.c290.81KB

timer.py1.08KB

nms_wrapper.py866Byte

network.py4.31KB

im_transform.py973Byte

build.py6KB

bbox.pyx9.24KB

bbox.c449.75KB

__init__.py0Byte

pycocotools

UPSTREAM_REV80Byte

maskApi.h1.88KB

maskApi.c7.52KB

mask.py3.96KB

license.txt1.5KB

cocoeval.py20.28KB

coco.py15.08KB

_mask.pyx10.46KB

_mask.c583.59KB

__init__.py21Byte

nms

py_cpu_nms.py1.03KB

nms_kernel.cu4.95KB

gpu_nms.pyx1.08KB

gpu_nms.hpp146Byte

cpu_nms.pyx2.19KB

__init__.py0Byte

.gitignore15Byte

layers

__init__.py0Byte

roi_pooling

roi_pool_py.py2.21KB

roi_pool.py3.17KB

build.py822Byte

__init__.py0Byte

src

roi_pooling_cuda.h420Byte

roi_pooling_cuda.c2.75KB

roi_pooling.h178Byte

roi_pooling.c4.01KB

cuda

roi_pooling_kernel.h767Byte

roi_pooling_kernel.cu7.82KB

_ext

__init__.py0Byte

roi_pooling

__init__.py385Byte

reorg

reorg_layer.py1.59KB

build.py802Byte

__init__.py0Byte

src

reorg_cuda_kernel.h251Byte

reorg_cuda_kernel.cu1.8KB

reorg_cuda.h122Byte

reorg_cuda.c453Byte

reorg_cpu.h123Byte

reorg_cpu.c1.02KB

_ext

__init__.py0Byte

reorg_layer

__init__.py385Byte

demo

scream.jpg170.42KB

ragged-edge-london-office-6.jpg595.12KB

person.jpg111.21KB

horses.jpg130.37KB

giraffe.jpg373.99KB

eagle.jpg138.56KB

dog.jpg159.92KB

2007_000039.jpg63.15KB

out

scream.jpg72.08KB

ragged-edge-london-office-6.jpg1.4MB

person.jpg121.59KB

horses.jpg145.69KB

giraffe.jpg231.73KB

eagle.jpg155.64KB

dog.jpg181.9KB

2007_000039.jpg66.52KB

datasets

voc_eval.py7.02KB

pascal_voc.py10.63KB

imdb.py5.02KB

__init__.py0Byte

cfgs

config_voc.py561Byte

config.py2.7KB

__init__.py0Byte

exps

darknet19_exp2.py447Byte

darknet19_exp1.py447Byte

__init__.py0Byte

chapter5

ssd-pytorch

train.py8.07KB

test.py3.78KB

ssd.py7.15KB

README.md7.18KB

LICENSE1.06KB

eval.py15.5KB

.gitignore1.42KB

.gitattributes110Byte

weights

vgg16_reducedfc.pth78.14MB

utils

augmentations.py13.15KB

__init__.py42Byte

__pycache__

augmentations.cpython-35.pyc15.66KB

__init__.cpython-35.pyc182Byte

layers

box_utils.py9.61KB

__init__.py48Byte

modules

multibox_loss.py5.82KB

l2norm.py758Byte

__init__.py105Byte

__pycache__

multibox_loss.cpython-35.pyc4.18KB

l2norm.cpython-35.pyc1.3KB

__init__.cpython-35.pyc262Byte

functions

prior_box.py1.95KB

detection.py2.63KB

__init__.py97Byte

__pycache__

prior_box.cpython-35.pyc1.91KB

detection.cpython-35.pyc2.51KB

__init__.cpython-35.pyc259Byte

__pycache__

box_utils.cpython-35.pyc8.37KB

__init__.cpython-35.pyc175Byte

doc

ssd.png71.03KB

SSD.jpg47.22KB

detection_examples.png1.96MB

detection_example2.png318.78KB

detection_example.png365.21KB

demo

live.py3KB

demo.ipynb1.26MB

__init__.py0Byte

data

voc0712.py6.4KB

example.jpg136.8KB

config.py726Byte

__init__.py1.31KB

scripts

VOC2012.sh763Byte

VOC2007.sh971Byte

COCO2014.sh1.91KB

__pycache__

voc0712.cpython-35.pyc6.82KB

config.cpython-35.pyc948Byte

coco.cpython-35.pyc7.78KB

__init__.cpython-35.pyc1.82KB

__pycache__

ssd.cpython-35.pyc6.65KB

.idea

workspace.xml21.11KB

vcs.xml183Byte

ssd.pytorch-master.iml398Byte

modules.xml288Byte

misc.xml185Byte

encodings.xml135Byte

dssd-pytorch

tcb.py722Byte

arm.py764Byte

chapter4

faster-rcnn-pytorch

trainval_net.py14.75KB

test_net.py11.89KB

requirements.txt80Byte

README.md6.86KB

LICENSE1.04KB

demo.py13.36KB

_init_paths.py312Byte

.gitignore2.82KB

logs

vgg_voc

events.out.tfevents.1542983031.aizz291Byte

events.out.tfevents.1542007867.aizz567Byte

events.out.tfevents.1542007598.aizz25Byte

events.out.tfevents.1542007525.aizz25Byte

events.out.tfevents.1542007423.aizz25Byte

events.out.tfevents.1542007135.aizz25Byte

events.out.tfevents.1542006392.aizz25Byte

events.out.tfevents.1541646048.aizz1.24MB

events.out.tfevents.1541645839.aizz25Byte

events.out.tfevents.1541645748.aizz25Byte

events.out.tfevents.1541645707.aizz25Byte

lib

setup.py4.69KB

make.sh1.25KB

roi_data_layer

roidb.py4KB

roibatchLoader.py8.59KB

minibatch.py2.85KB

__init__.py248Byte

pycocotools

UPSTREAM_REV80Byte

maskApi.h1.88KB

maskApi.c7.52KB

mask.py3.95KB

license.txt1.5KB

cocoeval.py19.44KB

coco.py14.71KB

_mask.pyx10.46KB

__init__.py21Byte

model

__init__.py0Byte

utils

net_utils.py7.32KB

logger.py2.41KB

config.py11.54KB

blob.py1.6KB

bbox.pyx3.35KB

__init__.py0Byte

.gitignore15Byte

rpn

rpn.py4.19KB

proposal_target_layer_cascade.py9.1KB

proposal_layer.py6.87KB

generate_anchors.py3.17KB

bbox_transform.py9.07KB

anchor_target_layer.py8.79KB

__init__.py0Byte

roi_pooling

build.py875Byte

__init__.py0Byte

src

roi_pooling_kernel.h767Byte

roi_pooling_kernel.cu9.35KB

roi_pooling_cuda.h420Byte

roi_pooling_cuda.c2.77KB

roi_pooling.h178Byte

roi_pooling.c4.01KB

modules

roi_pool.py524Byte

__init__.py0Byte

functions

roi_pool.py1.73KB

__init__.py0Byte

_ext

__init__.py0Byte

roi_pooling

__init__.py385Byte

roi_crop

make.sh219Byte

build.py881Byte

__init__.py0Byte

src

roi_crop_cuda_kernel.h2.75KB

roi_crop_cuda_kernel.cu16.77KB

roi_crop_cuda.h481Byte

roi_crop_cuda.c4.6KB

roi_crop.h659Byte

roi_crop.c22.57KB

modules

roi_crop.py287Byte

gridgen.py16.14KB

__init__.py0Byte

functions

roi_crop.py1002Byte

gridgen.py2.18KB

crop_resize.py1.51KB

__init__.py0Byte

_ext

__init__.py0Byte

roi_crop

__init__.py382Byte

crop_resize

__init__.py310Byte

roi_align

make.sh211Byte

build.py902Byte

__init__.py0Byte

src

roi_align_kernel.h1.23KB

roi_align_kernel.cu7.55KB

roi_align_cuda.h369Byte

roi_align_cuda.c2.37KB

roi_align.h361Byte

roi_align.c7.39KB

modules

roi_align.py1.63KB

__init__.py0Byte

functions

roi_align.py1.96KB

__init__.py0Byte

_ext

__init__.py0Byte

roi_align

__init__.py383Byte

nms

nms_wrapper.py757Byte

nms_kernel.cu4.95KB

nms_gpu.py299Byte

nms_cpu.py862Byte

make.sh209Byte

build.py850Byte

__init__.py0Byte

.gitignore15Byte

src

nms_cuda_kernel.h206Byte

nms_cuda_kernel.cu5.49KB

nms_cuda.h272Byte

_ext

__init__.py0Byte

nms

__init__.py377Byte

faster_rcnn

vgg16.py2.06KB

resnet.py8.58KB

faster_rcnn.py5.65KB

__init__.py0Byte

datasets

voc_eval.py6.5KB

vg_eval.py4.08KB

vg.py16.39KB

pascal_voc_rbg.py10.97KB

pascal_voc.py14.74KB

imdb.py8.9KB

imagenet.py8.22KB

factory.py2.61KB

ds_utils.py1.37KB

coco.py11.77KB

__init__.py248Byte

VOCdevkit-matlab-wrapper

xVOCap.m258Byte

voc_eval.m1.3KB

get_voc_opts.m231Byte

tools

mcg_munge.py1.46KB

images

img4_det_res101.jpg89.3KB

img4_det.jpg89.3KB

img4.jpg83KB

img3_det_res101.jpg104.86KB

img3_det.jpg104.86KB

img3.jpg100.36KB

img2_det_res101.jpg111.4KB

img2_det.jpg111.4KB

img2.jpg110.64KB

img1_det_res101.jpg83.85KB

img1_det.jpg83.85KB

img1.jpg76.92KB

cfgs

vgg16.yml287Byte

res50.yml347Byte

res101_ls.yml439Byte

res101.yml363Byte

chapter3

vgg.py938Byte

resnet_bottleneck.py966Byte

inceptionv2.py1.25KB

inceptionv1.py1.3KB

fpn.py3.15KB

detnet_bottleneck.py1.13KB

densenet_block.py1.11KB

chapter2

visdom.py366Byte

perception_sequential.py375Byte

perception.py732Byte

mlp.py437Byte

chapter1

model-evaluation

README.md315Byte

evaluation.py464Byte

evaluation.ipynb65.1KB

lib

utils.pyc0Byte

utils.py0Byte

Evaluator.pyc13.28KB

Evaluator.py4.43KB

detection.pyc13.06KB

detection.py3.64KB

__pycache__

utils.cpython-36.pyc5.37KB

Evaluator.cpython-36.pyc3.85KB

detection.cpython-36.pyc2.86KB

data

results

class2.png14.99KB

class1.png14.96KB

groundtruths

1.txt110Byte

detections

1.txt162Byte

conf

conf.yaml459Byte

arial.ttf304.33KB

02.机器学习相关书籍

图解机器学习.pdf59.4MB

凸优化.pdf5.73MB

机器学习在量化投资中的应用研究_汤凌冰著_北京：电子工业出版社_2014.11_13662591_P157.pdf25.58MB

机器学习实战.pdf13.41MB

机器学习实践指南++案例应用解析+麦好.pdf59.27MB

机器学习个人笔记完整版2.5.pdf7.75MB

机器学习导论原书第2版.pdf77.76MB

机器学习〔中文版〕.pdf9.91MB

机器学习_周志华.pdf37.53MB

吴恩达《MachineLearningYearning》完整中文版

吴恩达MLY

MLY-zh-cn.pdf5MB

《跟着迪哥学Python数据分析与机器学习实战》

《跟着迪哥学Python数据分析与机器学习实战》PDF+唐宇迪.pdf98.83MB

《跟着迪哥学Python数据分析与机器学习实战》.mobi67.29MB

《跟着迪哥学Python数据分析与机器学习实战》.epub40.11MB

01.Python基础书籍

《Python基础教程（第3版）》

源代码.zip87.95KB

Python基础教程（第3版）高清英文版.pdf5.96MB

01.人工智能行业报告

53份人工智能行业报告.zip129.49MB

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