AI精选付费资料包（37(1)

《AI精选付费资料包（37(1)》相关资源介绍

文件夹/文件数量：265个文件夹，384个文件
文件大小：21.30 GB

• 📁 AI精选付费资料包（37(1).4GB）（合集）
•     📁 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.pdf
•                 📄 6-支持向量机.pdf
•                 📄 4-聚类算法.pdf
•                 📄 9-LDA与PCA算法.pdf
•                 📄 7-推荐系统.pdf
•                 📄 8-xgboost.pdf
•                 📄 3-决策树与集成算法.pdf
•                 📄 12-word2vec.pdf
•                 📄 2-回归算法.pdf
•                 📄 1-AI入学指南.pdf
•                 📄 5-贝叶斯算法.pdf
•                 📄 时间序列分析.pdf
•                 📄 11-神经网络.pdf
•                 📄 10-EM算法.pdf
•             📁 部分代码资料
•                 📁 10-决策树原理
•                     📄 3-决策树与集成算法.pdf
•                 📁 6-逻辑回归实验分析
•                     📄 逻辑回归-实验.zip
•                 📁 12-决策树实验分析
•                     📄 决策树算法-实验.zip
•                 📁 8-Kmeans代码实现
•                     📄 Kmeans-代码实现.zip
•                 📁 5-逻辑回归代码实现
•                     📄 逻辑回归-代码实现.zip
•                 📁 14-集成算法实验分析
•                     📁 mldata
•                     📄 随机森林与集成算法-实验.zip
•                 📁 3-线性回归实验分析
•                     📄 线性回归-实验.zip
•                 📁 3-模型评估方法
•                     📁 img
•                     📄 模型评估方法.ipynb
•                 📁 11-决策树代码实现
•                     📄 决策树-代码实现.zip
•                 📁 1-线性回归原理推导
•                     📄 2-回归算法.pdf
•                 📁 9-聚类算法实验分析
•                     📁 mldata
•                     📄 聚类算法-实验.zip
•                 📁 2-线性回归代码实现
•                     📄 线性回归-代码实现.zip
•                 📁 7-聚类算法-Kmeans&Dbscan原理
•                     📄 4-聚类算法.pdf
•                 📁 13-集成算法原理
•                     📄 3-决策树与集成算法.pdf
•                 📁 15-支持向量机原理推导
•                     📄 6-支持向量机.pdf
•         📁 01.机器学习经典算法精讲视频课程
•             📁 第三章：模型评估方法
•                 📁 分类模型评估
•                     📁 5-混淆矩阵
•                     📁 6-评估指标对比分析
•                     📁 7-阈值对结果的影响
•                     📁 2-数据集切分
•                     📁 4-交叉验证实验分析
•                     📁 3-交叉验证的作用
•                     📁 1-Sklearn工具包简介
•                     📁 8-ROC曲线
•             📁 第十二章：决策树代码实现
•                 📁 第五章：决策树
•                     📁 3-整体框架逻辑
•                     📁 5-数据集切分
•                     📁 2-递归生成树节点
•                     📁 1-整体模块概述
•                     📁 4-熵值计算
•                     📁 7-测试算法效果
•                     📁 6-完成树模型构建
•             📁 第一章：线性回归原理推导
•                 📄 8-优化参数设置.mp4
•                 📄 0-课程简介.mp4
•                 📄 6-梯度下降通俗解释.mp4
•                 📄 1-回归问题概述.mp4
•                 📄 5-参数求解.mp4
•                 📄 3-独立同分布的意义.mp4
•                 📄 2-误差项定义.mp4
•                 📄 4-似然函数的作用.mp4
•                 📄 7参数更新方法.mp4
•             📁 第十一章：决策树原理
•                 📄 1-决策树算法概述.mp4
•                 📄 7-后剪枝方法.mp4
•                 📄 8-回归问题解决.mp4
•                 📄 5-信息增益率与gini系数.mp4
•                 📄 2-熵的作用.mp4
•                 📄 4-决策树构造实例.mp4
•                 📄 3-信息增益原理.mp4
•                 📄 6-预剪枝方法.mp4
•             📁 第九章：Kmeans代码实现
•                 📁 第三章：聚类-Kmeans
•                     📁 6-聚类效果展示
•                     📁 4-算法迭代更新
•                     📁 5-鸢尾花数据集聚类任务
•                     📁 1-Kmeans算法模块概述
•                     📁 3-样本点归属划分
•                     📁 2-计算得到簇中心点
•             📁 第八章：聚类算法-Kmeans&Dbscan原理
•                 📄 3-KMEANS迭代可视化展示.mp4
•                 📄 2-KMEANS工作流程.mp4
•                 📄 4-DBSCAN聚类算法.mp4
•                 📄 6-DBSCAN可视化展示.mp4
•                 📄 1-KMEANS算法概述.mp4
•                 📄 5-DBSCAN工作流程.mp4
•             📁 第十章：聚类算法实验分析
•                 📁 聚类
•                     📁 2-聚类结果展示
•                     📁 4-不稳定结果
•                     📁 10-半监督学习
•                     📁 3-建模流程解读
•                     📁 1-Kmenas算法常用操作
•                     📁 11-DBSCAN算法
•                     📁 6-如何找到合适的K值
•                     📁 7-轮廓系数的作用
•                     📁 9-应用实例-图像分割
•                     📁 5-评估指标-Inertia
•                     📁 8-Kmenas算法存在的问题
•             📁 第七章：逻辑回归实验分析
•                 📄 2-概率结果随特征数值的变化.mp4
•                 📄 6-多分类-softmax.mp4
•                 📄 1-逻辑回归实验概述.mp4
•                 📄 5-分类决策边界展示分析.mp4
•                 📄 4-坐标棋盘制作.mp4
•                 📄 3-可视化展示.mp4
•             📁 第十三章：决策树实验分析
•                 📁 决策树
•                     📁 2-决策边界展示分析
•                     📁 4-回归树模型
•                     📁 3-树模型预剪枝参数作用
•                     📁 1-树模型可视化展示
•             📁 第六章：逻辑回归代码实现
•                 📁 第二章：逻辑回归
•                     📁 10-准备测试数据
•                     📁 7-得出最终结果
•                     📁 1-多分类逻辑回归整体思路
•                     📁 8-鸢尾花数据集多分类任务
•                     📁 12-非线性决策边界
•                     📁 3-完成预测模块
•                     📁 5-迭代优化参数
•                     📁 4-优化目标定义
•                     📁 2-训练模块功能
•                     📁 11-决策边界绘制
•                     📁 6-梯度计算
•                     📁 9-训练多分类模型
•             📁 第五章：逻辑回归原理推导
•                 📄 2-化简与求解.mp4
•                 📄 1-逻辑回归算法原理.mp4
•             📁 第二章：线性回归代码实现
•                 📁 第一章：线性回归
•                     📁 5-数据与标签定义
•                     📁 10-非线性回归
•                     📁 9-多特征回归模型
•                     📁 4-损失与预测模块
•                     📁 6-训练线性回归模型
•                     📁 8-整体流程debug解读
•                     📄 1-线性回归整体模块概述.mp4
•                     📄 7-得到线性回归方程.mp4
•                     📄 3-实现梯度下降优化模块.mp4
•                     📄 2-初始化步骤.mp4
•             📁 课程简介
•                 📁 项目截图
•                     📄 QQ截图20190624141231.png
•                     📄 QQ截图20190624141129.png
•                     📄 1.png
•                     📄 QQ截图20190624141428.png
•                     📄 QQ截图20190624141330.png
•                 📄 Python机器学习实训营.docx
•             📁 第四章：线性回归实验分析
•                 📁 线性回归
•                     📁 9-多项式回归
•                     📁 7-MiniBatch方法
•                     📁 10-模型复杂度
•                     📁 3-预处理对结果的影响
•                     📁 12-正则化的作用
•                     📁 14-实验总结
•                     📁 5-学习率对结果的影响
•                     📁 2-参数直接求解方法
•                     📁 4-梯度下降模块
•                     📁 6-随机梯度下降得到的效果
•                     📁 11-样本数量对结果的影响
•                     📁 8-不同策略效果对比
•                     📁 13-岭回归与lasso
•                     📄 1-实验目标分析.mp4
•     📁 六：计算机视觉实战项目
•         📁 07.MASK-RCNN课程资料
•             📁 第六章：物体检测-faster-rcnn
•                 📄 FasterRcnn.zip
•                 📄 Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks.pdf
•                 📄 iccv15_tutorial_training_rbg.pdf
•                 📄 faster-rcnn.pptx
•             📄 第四章：练手小项目-人体姿态识别demo.zip
•             📄 第二章：MaskRcnn网络框架源码详解.zip
•             📄 第三章：基于MASK-RCNN框架训练自己的数据与任务.zip
•             📄 第五章：迁移学习.zip
•         📁 04.Unet图像分割实战视频课程
•             📄 4.mp4
•             📄 5.mp4
•             📄 2.mp4
•             📄 3.mp4
•             📄 1.mp4
•         📁 02.YOLOV5目标检测视频课程
•             📄 4-各版本模型介绍.mp4
•             📄 2-数据与标签配置方法.mp4
•             📄 5-项目参数配置.mp4
•             📄 7-输出结果与项目总结.mp4
•             📄 3-标签转格式脚本制作.mp4
•             📄 1.任务需求与项目概述.mp4
•             📄 6-缺陷检测模型培训.mp4
•         📁 08.Unet图像分割课程资料
•             📄 unet++.zip
•             📄 深度学习分割任务.pdf
•         📁 06.YOLOV5目标检测课程资料
•             📄 PyTorch-YOLOv3.zip
•             📄 NEU-DET.zip
•             📄 YOLO.pdf
•         📁 01.OpenCV图像处理实战视频课程
•             📁 项目实战一：信用卡数字识别
•                 📁 2-环境配置与预处理
•                     📄 2-环境配置与预处理.mp4
•                 📁 1-总体流程与方法讲解
•                     📄 总体流程与方法讲解.mp4
•                 📁 4-输入数据处理方法
•                     📄 4-输入数据处理方法.mp4
•                 📁 5-模板匹配得出识别结果
•                     📄 5-模板匹配得出识别结果.mp4
•                 📁 3-模板处理方法
•                     📄 3-模板处理方法.mp4
•             📁 项目实战二：文档扫描OCR识别
•                 📁 3-原始与变换坐标计算
•                     📄 3-原始与变换坐标计算.mp4
•                 📁 2-文档轮廓提取
•                     📄 2-文档轮廓提取.mp4
•                 📁 6-文档扫描识别效果
•                     📄 6-文档扫描识别效果.mp4
•                 📁 1-整体流程演示
•                     📄 1-整体流程演示.mp4
•                 📁 4-透视变换结果
•                     📄 4-透视变换结果.mp4
•                 📁 5-tesseract-ocr安装配置
•                     📄 5-tesseract-ocr安装配置.mp4
•             📁 项目实战三：全景图像拼接
•                 📁 4-流程解读
•                     📄 4-流程解读.mp4
•                 📁 2-图像拼接方法
•                     📄 2-图像拼接方法.mp4
•                 📁 2-RANSAC算法
•                     📄 2-RANSAC算法.mp4
•                 📁 1-特征匹配方法
•                     📄 1-特征匹配方法.mp4
•             📁 项目实战五：答题卡识别判卷
•                 📁 2-预处理操作
•                     📄 2-预处理操作.mp4
•                 📁 4-选项判断识别
•                     📄 4-选项判断识别.mp4
•                 📁 1-整体流程与效果概述
•                     📄 1-整体流程与效果概述.mp4
•                 📁 3-填涂轮廓检测
•                     📄 3-填涂轮廓检测.mp4
•             📁 项目实战四：停车场车位识别
•                 📁 3-图像数据预处理
•                     📄 3-图像数据预处理.mp4
•                 📁 4-车位直线检测
•                     📄 4-车位直线检测.mp4
•                 📁 8-基于视频的车位检测
•                     📄 8-基于视频的车位检测.mp4
•                 📁 7-识别模型构建
•                     📄 7-识别模型构建.mp4
•                 📁 2-所需数据介绍
•                     📄 2-所需数据介绍.mp4
•                 📁 1-任务整体流程
•                     📄 1-任务整体流程.mp4
•                 📁 6-车位区域划分
•                     📄 6-车位区域划分.mp4
•                 📁 5-按列划分区域
•                     📄 5-按列划分区域.mp4
•         📁 05.OpenCV图像处理课程资料
•             📄 第九章：项目实战-信用卡数字识别.zip
•             📄 第16-17章notebook课件.zip
•             📄 第十四章：项目实战-停车场车位识别.zip
•             📄 第二十章：人脸关键点定位.zip
•             📄 第十三章：案例实战-全景图像拼接.zip
•             📄 第11-12章notebook课件.zip
•             📄 第十八章：Opencv的DNN模块.zip
•             📄 第十章：项目实战-文档扫描OCR识别.zip
•             📄 第八章notebook课件.zip
•             📄 第十五章：项目实战-答题卡识别判卷.zip
•             📄 第2-7章notebook课件.zip
•             📄 第二十一章：项目实战-疲劳检测.zip
•         📁 03.MASK-RCNN目标检测实战视频课程
•             📁 第二章：MaskRcnn网络框架源码详解
•                 📁 11-RorAlign操作的效果
•                     📄 11-RorAlign操作的效果.mp4
•                 📁 9-正负样本选择与标签定义
•                     📄 9-正负样本选择与标签定义.mp4
•                 📁 1-FPN层特征提取原理解读
•                     📄 1-FPN层特征提取原理解读.mp4
•                 📁 3-生成框比例设置
•                     📄 3-生成框比例设置.mp4
•                 📁 2-FPN网络架构实现解读
•                     📄 2-FPN网络架构实现解读.mp4
•                 📁 12-整体框架回顾
•                     📄 12-整体框架回顾.mp4
•                 📁 5-RPN层的作用与实现解读
•                     📄 5-RPN层的作用与实现解读.mp4
•                 📁 7-Proposal层实现方法
•                     📄 7-Proposal层实现方法.mp4
•                 📁 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.mp4
•                 📁 8-DetectionTarget层的作用
•                     📄 8-DetectionTarget层的作用.mp4
•                 📁 10-RoiPooling层的作用与目的
•                     📄 10-RoiPooling层的作用与目的.mp4
•                 📁 6-候选框过滤方法
•                     📄 6-候选框过滤方法.mp4
•             📁 第三章：基于MASK-RCNN框架训练自己的数据与任务
•                 📁 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.mp4
•                 📁 3-完成训练数据准备工作
•                     📄 3-完成训练数据准备工作.mp4
•                 📁 4-maskrcnn源码修改方法
•                     📄 4-maskrcnn源码修改方法.mp4
•                 📁 6-测试与展示模块
•                     📄 6-测试与展示模块.mp4
•                 📁 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.mp4
•                 📁 1-Labelme工具安装
•                     📄 1-Labelme工具安装.mp4
•             📁 第六章：必备基础-物体检测FasterRcnn系列
•                 📁 5-论文解读-2-RPN网络结构
•                     📄 论文解读-2-RPN网络结构.mp4
•                 📁 4-论文解读-1-论文整体概述
•                     📄 论文解读-1.mp4
•                 📁 3-三代算法-3-faster-rcnn概述
•                     📄 三代算法-3-faster-rcnn概述.mp4
•                 📁 6-论文解读-3-损失函数定义
•                     📄 论文解读-3-损失函数定义.mp4
•                 📁 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.mp4
•                 📁 1-三代算法-1-物体检测概述
•                     📄 三代算法-1-物体检测概述.mp4
•                 📁 7-论文解读-4-网络细节
•                     📄 论文解读-4-网络细节.mp4
•             📁 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
•                     📁 6-shortcut模块
•                     📁 5-Resnet基本处理操作
•                     📁 2-迁移学习策略
•                     📁 1-迁移学习的目标
•                     📁 4-Resnet网络细节
•                     📁 3-Resnet原理
•                     📁 7-加载训练好的权重
•                 📁 2-开源项目数据集
•                     📄 0-开源项目数据集.mp4
•                 📁 0-课程简介
•                     📄 0-课程简介.mp4
•                 📁 3-参数配置
•                     📄 0-参数配置.mp4
•                 📁 1-Mask-Rcnn开源项目简介
•                     📄 0-Mask-Rcnn开源项目简介.mp4
•             📁 第四章：练手小项目-人体姿态识别demo
•                 📁 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.mp4
•                 📁 3-流程与结果演示
•                     📄 3-流程与结果演示.mp4
•                 📁 2-网络架构概述
•                     📄 2-网络架构概述.mp4
•     📁 三：超详细人工智能学习大纲
•         📄 人工智能大纲升级版本.pdf
•     📁 一：人工智能论文合集
•         📁 Resnet论文解读
•             📄 13-额外补充-Resnet论文解读.mp4
•         📁 cvpr2021
•             📁 解压密码：cvpr2021
•         📁 ICCV2021
•             📁 解压密码： iccv2021
•         📁 CVPR行人重识别论文解读
•             📄 5. 4-基于图卷积构建人体拓扑关系.mp4
•             📄 6. 5-图卷积模块实现方法.mp4
•             📄 1. 1-关键点位置特征构建.mp4
•             📄 2. 2-图卷积与匹配的作用.mp4
•             📄 4. 3-局部特征热度图计算.mp4
•         📁 深度学习论文精讲-BERT模型
•             📄 8. 7-BERT模型训练策略.mp4
•             📄 5. 4-预训练模型的作用.mp4
•             📄 7. 6-向量特征编码方法.mp4
•             📄 2. 1-论文讲解思路概述.mp4
•             📄 1. 课程介绍.mp4
•             📄 3. 2-BERT模型摘要概述.mp4
•             📄 9. 8-论文总结分析.mp4
•             📄 4. 3-模型在NLP领域应用效果.mp4
•             📄 6. 5-输入数据特殊编码字符解析.mp4
•         📁 CNN_不能错过的10篇论文
•             📄 1512.03385v1_Deep Residual Learning for Image Recognition.pdf
•             📄 1311.2901v3_Visualizing and Understanding Convolutional Networks.pdf
•             📄 1504.08083_Fast R-CNN.pdf
•             📄 1506.02025_Spatial Transformer Networks.pdf
•             📄 1506.01497v3_Faster R-CNN.pdf
•             📄 1406.2661v1_Generative Adversarial Nets.pdf
•             📄 1311.2524v5_R_CNN.pdf
•             📄 1412.2306v2_Deep Visual-Semantic Alignments for Generating Image Descriptions.pdf
•             📄 1409.1556v6_VERY DEEP CONVOLUTIONAL Networks.pdf
•             📄 Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf
•             📄 4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
•         📁 图神经网络（GNN）100篇论文集
•             📁 Applications
•                 📁 science
•                     📄 Learning Deep Generative Models of Graphs.pdf
•                     📄 Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.pdf
•                     📄 Convolutional networks on graphs for learning molecular fingerprints.pdf
•                     📄 Effective Approaches to Attention-based Neural Machine Translation.pdf
•                     📄 A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.pdf
•                     📄 Learning model-based planning from scratch.pdf
•                     📄 A Compositional Object-Based Approach to Learning Physical Dynamics.pdf
•                     📄 Symbolic Graph Reasoning Meets Convolutions.pdf
•                     📄 Adversarial Attack on Graph Structured Data.pdf
•                     📄 Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf
•                     📄 Learning Multiagent Communication with Backpropagation.pdf
•                     📄 Structured Dialogue Policy with Graph Neural Networks.pdf
•                     📄 Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.pdf
•                     📄 Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification.pdf
•                     📄 Discovering objects and their relations from entangled scene representations.pdf
•                     📄 Molecular Graph Convolutions- Moving Beyond Fingerprints.pdf
•                     📄 Neural Combinatorial Optimization with Reinforcement Learning.pdf
•                     📄 GraphRNN- Generating Realistic Graphs with Deep Auto-regressive Models.pdf
•                     📄 Traffic Graph Convolutional Recurrent Neural Network- A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.pdf
•                     📄 Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.pdf
•                     📄 Spatio-Temporal Graph Convolutional Networks- A Deep Learning Framework for Traffic Forecasting.pdf
•                     📄 Metacontrol for Adaptive Imagination-Based Optimization.pdf
•                     📄 Self-Attention with Relative Position Representations.pdf
•                     📄 Relational inductive bias for physical construction in humans and machines.pdf
•                     📄 NerveNet Learning Structured Policy with Graph Neural Networks.pdf
•                     📄 Deep Graph Infomax.pdf
•                     📄 Understanding Kin Relationships in a Photo.pdf
•                     📄 Learning Human-Object Interactions by Graph Parsing Neural Networks.pdf
•                     📄 Neural Module Networks.pdf
•                     📄 DeepInf- Modeling influence locality in large social networks.pdf
•                     📄 Neural Relational Inference for Interacting Systems.pdf
•                     📄 Attention, Learn to Solve Routing Problems!.pdf
•                     📄 Translating Embeddings for Modeling Multi-relational Data.pdf
•                     📄 Learning a SAT Solver from Single-Bit Supervision.pdf
•                     📄 Visual Interaction Networks- Learning a Physics Simulator from Vide.o.pdf
•                     📄 Combining Neural Networks with Personalized PageRank for Classification on Graphs.pdf
•                     📄 Learning to Represent Programs with Graphs.pdf
•                     📄 Semi-supervised User Geolocation via Graph Convolutional Networks.pdf
•                     📄 Hyperbolic Attention Networks.pdf
•                     📄 Cross-Sentence N-ary Relation Extraction with Graph LSTMs.pdf
•                     📄 Graph Convolutional Matrix Completion.pdf
•                     📄 Interaction Networks for Learning about Objects, Relations and Physics.pdf
•                     📄 Constructing Narrative Event Evolutionary Graph for Script Event Prediction.pdf
•                     📄 Protein Interface Prediction using Graph Convolutional Networks.pdf
•                     📄 Action Schema Networks- Generalised Policies with Deep Learning.pdf
•                     📄 Situation Recognition with Graph Neural Networks.pdf
•                     📄 Relational Deep Reinforcement Learning.pdf
•                     📄 Relational neural expectation maximization- Unsupervised discovery of objects and their interactions.pdf
•                     📄 Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks.pdf
•                     📄 Beyond Categories- The Visual Memex Model for Reasoning About Object Relationships.pdf
•                 📁 image
•                     📁 Visual Question Answering
•                     📁 Image classification
•                     📁 Object Detection
•                     📁 Interaction Detection
•                     📁 Region Classification
•                     📁 Semantic Segmentation
•                 📁 graph generation
•                     📄 Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.pdf
•                     📄 NetGAN- Generating Graphs via Random Walks(1).pdf
•                     📄 MolGAN- An implicit generative model for small molecular graphs(1).pdf
•                 📁 combinatorial optimization
•                     📄 Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search(1).pdf
•                     📄 Learning Combinatorial Optimization Algorithms over Graphs.pdf
•                 📁 knowledge graph
•                     📄 Deep Reasoning with Knowledge Graph for Social Relationship Understanding.pdf
•                     📄 Representation learning for visual-relational knowledge graphs.pdf
•                     📄 Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering.pdf
•                     📄 The More You Know- Using Knowledge Graphs for Image Classification.pdf
•                     📄 Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.pdf
•                     📄 Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs.pdf
•                     📄 Knowledge Transfer for Out-of-Knowledge-Base Entities - A Graph Neural Network Approach.pdf
•                     📄 Dynamic Graph Generation Network- Generating Relational Knowledge from Diagrams.pdf
•                     📄 Multi-Label Zero-Shot Learning with Structured Knowledge Graphs.pdf
•                 📁 text
•                     📄 Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.pdf
•                     📄 Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling.pdf
•                     📄 Graph Convolutional Networks with Argument-Aware Pooling for Event Detection.pdf
•                     📄 N-ary relation extraction using graph state LSTM.pdf
•                     📄 Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks..pdf
•                     📄 A Graph-to-Sequence Model for AMR-to-Text Generation.pdf
•                     📄 Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.pdf
•                     📄 Jointly Multiple Events Extraction via Attention-based Graph.pdf
•                     📄 Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.pdf
•                     📄 End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.pdf
•                     📄 Graph Convolutional Networks for Text Classification.pdf
•                     📄 Recurrent Relational Networks.pdf
•             📁 Models
•                 📁 graph_type
•                     📁 directed graph
•                     📁 edge-informative graph
•                     📄 Graph Capsule Convolutional Neural Networks.pdf
•                     📄 How Powerful are Graph Neural Networks-.pdf
•                     📄 Adaptive Graph Convolutional Neural Networks.pdf
•                     📄 Graph Neural Networks for Ranking Web Pages.pdf
•                     📄 Mean-field theory of graph neural networks in graph partitioning.pdf
•                     📄 Graph Partition Neural Networks for Semi-Supervised Classification.pdf
•                     📄 Graph Neural Networks for Object Localization.pdf
•                 📁 propagation_type
•                     📁 convolution
•                     📁 attention
•                     📁 gate
•                     📁 skip
•                 📁 training methods
•                     📁 receptive field control
•                     📁 boosting
•                     📁 neighborhood sampling
•                     📄 Covariant Compositional Networks For Learning Graphs.pdf
•                     📄 Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction.pdf
•                     📄 Learning Steady-States of Iterative Algorithms over Graphs.pdf
•                     📄 Graphical-Based Learning Environments for Pattern Recognition.pdf
•                     📄 Neural networks for relational learning- an experimental comparison.pdf
•                     📄 Hierarchical Graph Representation Learning with Differentiable Pooling.pdf
•                 📁 others
•                     📄 Deep Sets.pdf
•                     📄 Diffusion-Convolutional Neural Networks.pdf
•                     📄 A Comparison between Recursive Neural Networks and Graph Neural Networks.pdf
•                     📄 Contextual Graph Markov Model- A Deep and Generative Approach to Graph Processing.pdf
•                     📄 Deriving Neural Architectures from Sequence and Graph Kernels.pdf
•                     📄 CelebrityNet- A Social Network Constructed from Large-Scale Online Celebrity Images.pdf
•                     📄 A new model for learning in graph domains.pdf
•                     📄 Geometric deep learning on graphs and manifolds using mixture model cnns.pdf
•             📁 Survey
•                 📁 极力推荐
•                     📄 Relational Inductive Biases, Deep Learning, and Graph Networks.pdf
•                     📄 The Graph Neural Network Model.pdf
•                     📄 Non-local Neural Networks.pdf
•                     📄 Graph Neural Networks：A Review of Methods and Applications.pdf
•                 📁 一般推荐
•                     📄 Geometric Deep Learning- Going beyond Euclidean data.pdf
•                     📄 Deep Learning on Graphs- A Survey.pdf
•                     📄 A Comprehensive Survey on Graph Neural Networks.pdf
•                     📄 Computational Capabilities of Graph Neural Networks(1).pdf
•                     📄 Neural Message Passing for Quantum Chemistry.pdf
•             📄 论文集索引.jpg
•     📁 五：深度学习神经网络基础教程
•         📁 GAN对抗生成网络基础
•             📄 3 生成对抗网络.flv
•             📄 7 EM距离.flv
•             📄 9 GAN实战-1.flv
•             📄 11 WGAN实战-1.flv
•             📄 6 GAN训练难题.flv
•             📄 8 WGAN-GP原理.flv
•             📄 5 纳什均衡-2.flv
•             📄 2 画家的成长历程.flv
•             📄 4 纳什均衡-1.flv
•             📄 10 GAN实战-2.flv
•             📄 12 WGAN实战-2.flv
•             📄 1 数据的分布.flv
•         📁 CNN卷积神经网络基础
•             📄 22-ResNet实战-3.mp4
•             📄 14-经典卷积神经网络详解-1.mp4
•             📄 12-CIFAR100与VGG13实战-3.mp4
•             📄 17-BatchNorm-2.mp4
•             📄 9-池化与采样操作讲解.mp4
•             📄 20-ResNet实战-1.mp4
•             📄 23-ResNet实战-4.mp4
•             📄 15-经典卷积神经网络详解-2.mp4
•             📄 7-卷积神经网络图解-3.mp4
•             📄 2-卷积运算详解-2.mp4
•             📄 19-ResNet, DenseNet详解.mp4
•             📄 21-ResNet实战-2.mp4
•             📄 11-CIFAR100与VGG13实战-2.mp4
•             📄 4-卷积运算详解-4.mp4
•             📄 8-卷积神经网络图解-4.mp4
•             📄 10-CIFAR100与VGG13实战-1.mp4
•             📄 18-ResNet, DenseNet详解.mp4
•             📄 1-卷积运算详解-1.mp4
•             📄 3-卷积运算详解-3.mp4
•             📄 6-卷积神经网络图解-2.mp4
•             📄 5-卷积神经网络图解-1.mp4
•             📄 13-CIFAR100与VGG13实战-4.mp4
•         📁 神经网络模型基础课件资料
•             📁 CNN+RNN+GAN
•                 📁 课程安装软件-Win10
•                     📄 cuda_10.0.130_411.31_win10.exe
•                     📄 pycharm-community-2019.1.1.exe
•                     📄 Anaconda3-2019.03-Windows-x86_64.exe
•                     📄 cudnn-10.0-windows10-x64-v7.5.0.56 (1).zip
•                 📁 课程安装软件-Ubuntu 18.04
•                     📄 cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb
•                     📄 Anaconda3-2019.03-Linux-x86_64.sh
•                     📄 cudnn-10.0-linux-x64-v7.5.0.56.tgz
•                 📄 源代码和PPT在Github下载.txt
•             📄 Deep-Learning-with-PyTorch-Tutorials.zip
•         📁 RNN循环神经网络基础
•             📄 2. 课时2 循环神经网络基本原理-1.mp4
•             📄 4. 课时4 循环神经网络中Layer使用-1.mp4
•             📄 1. 课时1 时间序列介绍.mp4
•             📄 11. 课时11 项目实战-情感分类问题.mp4
•             📄 9. 课时9 LSTM中Layer的使用.mp4
•             📄 3. 课时3 循环神经网络基本原理-2.mp4
•             📄 8. 课时8 LSTM基本原理-2.mp4
•             📄 10. 课时10 RNN训练难题—梯度弥散与梯度爆炸.mp4
•             📄 6. 课时6 项目实战-时间序列预测问题.mp4
•             📄 5. 课时5 循环神经网络中Layer的使用-2.mp4
•             📄 7. 课时7 LSTM基本原理-1.mp4
•     📁 二：AI必读经典书籍
•         📁 01.人工智能行业报告
•             📄 53份人工智能行业报告.zip
•         📁 02.AI必读经典书籍
•             📁 01.Python基础书籍
•                 📁 《Python基础教程（第3版）》
•                     📄 Python基础教程（第3版）高清英文版.pdf
•                     📄 源代码.zip
•             📁 03.深度学习相关书籍
•                 📁 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.pdf
•                     📄 d2l-zh-pytorch.pdf
•                     📄 d2l-en-pytorch.pdf
•                 📁 《深度学习之PyTorch物体检测实战》PDF+源代码
•                     📁 源代码
•                     📄 深度学习之PyTorch物体检测实战.pdf
•                     📄 深度学习之PyTorch物体检测实战论文导引.docx
•                     📄 深度学习之PyTorch物体检测实战.epub
•                     📄 深度学习之PyTorch物体检测实战.mobi
•                 📄 《TensorFlow 2.0深度学习算法实战教材》-中文版教材分享.pdf
•                 📄 深度学习技术图像处理入门 by 杨培文，胡博强 (z-lib.org).pdf
•                 📄 Tensorflow技术解析与实战.pdf
•                 📄 深度学习(花园书).pdf
•                 📄 《神经网络与深度学习》(邱锡鹏-20191121).pdf
•             📁 04.计算机视觉相关书籍
•                 📄 超详细的计算机视觉书籍.zip
•             📁 02.机器学习相关书籍
•                 📁 《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》.mobi
•                     📄 《跟着迪哥学 Python数据分析与机器学习实战》PDF+唐宇迪.pdf
•                     📄 《跟着迪哥学 Python数据分析与机器学习实战》.epub
•                 📁 吴恩达《Machine Learning Yearning》完整中文版
•                     📁 吴恩达MLY
•                 📄 机器学习导论 原书 第2版.pdf
•                 📄 机器学习个人笔记完整版2.5.pdf
•                 📄 图解机器学习.pdf
•                 📄 机器学习_周志华.pdf
•                 📄 机器学习〔中文版〕.pdf
•                 📄 机器学习实战.pdf
•                 📄 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.pdf
•                 📄 凸优化.pdf
•                 📄 机器学习实践指南++案例应用解析+麦好.pdf
•             📄 OpenCV书籍.rar

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