AI精选付费资料包（37.4GB） - 猎人搜索 轻松搜寻全网资源

• file:人工智能大纲升级版本.pdf
• file:PyTorch-YOLOv3.zip
• file:YOLO.pdf
• file:NEU-DET.zip
• file:5.mp4
• file:深度学习分割任务.pdf
• file:unet++.zip
• file:4-各版本模型介绍.mp4
• file:3-标签转格式脚本制作.mp4
• file:5-项目参数配置.mp4
• file:7-输出结果与项目总结.mp4
• file:1.任务需求与项目概述.mp4
• file:2-数据与标签配置方法.mp4
• file:第十四章：项目实战-停车场车位识别.zip
• file:第2-7章notebook课件.zip
• file:第十八章：Opencv的DNN模块.zip
• file:第二十章：人脸关键点定位.zip
• file:第三章：基于MASK-RCNN框架训练自己的数据与任务.zip
• file:第五章：迁移学习.zip
• file:第二章：MaskRcnn网络框架源码详解.zip
• file:第四章：练手小项目-人体姿态识别demo.zip
• file:OpenCV书籍.rar
• file:53份人工智能行业报告.zip
• file:论文集索引.jpg
• file:2. 2-图卷积与匹配的作用.mp4
• file:5. 4-基于图卷积构建人体拓扑关系.mp4
• file:1. 1-关键点位置特征构建.mp4
• file:4. 3-局部特征热度图计算.mp4
• file:13-额外补充-Resnet论文解读.mp4
• file:1409.1556v6_VERY DEEP CONVOLUTIONAL Networks.pdf
• file:1406.2661v1_Generative Adversarial Nets.pdf
• file:Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf
• file:1412.2306v2_Deep Visual-Semantic Alignments for Generating Image Descriptions.pdf
• file:1506.02025_Spatial Transformer Networks.pdf
• file:1311.2901v3_Visualizing and Understanding Convolutional Networks.pdf
• file:4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
• file:1506.01497v3_Faster R-CNN.pdf
• file:1512.03385v1_Deep Residual Learning for Image Recognition.pdf
• file:1311.2524v5_R_CNN.pdf
• file:3. 2-BERT模型摘要概述.mp4
• file:2. 1-论文讲解思路概述.mp4
• file:4. 3-模型在NLP领域应用效果.mp4
• file:6. 5-输入数据特殊编码字符解析.mp4
• file:Deep-Learning-with-PyTorch-Tutorials.zip
• file:9-池化与采样操作讲解.mp4
• file:7-卷积神经网络图解-3.mp4
• file:21-ResNet实战-2.mp4
• file:19-ResNet, DenseNet详解.mp4
• file:17-BatchNorm-2.mp4
• file:13-CIFAR100与VGG13实战-4.mp4
• file:12 WGAN实战-2.flv
• file:7 EM距离.flv
• file:4 纳什均衡-1.flv
• file:3 生成对抗网络.flv
• file:2 画家的成长历程.flv
• file:4. 课时4 循环神经网络中Layer使用-1.mp4
• file:1. 课时1 时间序列介绍.mp4
• file:10. 课时10 RNN训练难题—梯度弥散与梯度爆炸.mp4
• file:11. 课时11 项目实战-情感分类问题.mp4
• file:7. 课时7 LSTM基本原理-1.mp4
• file:FasterRcnn.zip
• file:Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks.pdf
• file:iccv15_tutorial_training_rbg.pdf
• file:深度学习技术图像处理入门 by 杨培文，胡博强 (z-lib.org).pdf
• file:《TensorFlow 2.0深度学习算法实战教材》-中文版教材分享.pdf
• file:Tensorflow技术解析与实战.pdf
• file:《神经网络与深度学习》(邱锡鹏-20191121).pdf
• file:机器学习导论 原书 第2版.pdf
• file:机器学习实践指南++案例应用解析+麦好.pdf
• file:图解机器学习.pdf
• file: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
• file:超详细的计算机视觉书籍.zip
• file:Python机器学习实训营.docx
• file:5-分类决策边界展示分析.mp4
• file:2-概率结果随特征数值的变化.mp4
• file:3-可视化展示.mp4
• file:1-逻辑回归实验概述.mp4
• file:6-多分类-softmax.mp4
• file:3-独立同分布的意义.mp4
• file:7参数更新方法.mp4
• file:6-梯度下降通俗解释.mp4
• file:5-信息增益率与gini系数.mp4
• file:2-KMEANS工作流程.mp4
• file:4-DBSCAN聚类算法.mp4
• file:6-支持向量机.pdf
• file:12-word2vec.pdf
• file:8-xgboost.pdf
• file:10-EM算法.pdf
• file:3-决策树与集成算法.pdf
• file:1-AI入学指南.pdf
• file:时间序列分析.pdf
• file:源代码和PPT在Github下载.txt
• file:0-Mask-Rcnn开源项目简介.mp4
• file:5-基于标注数据训练所需任务.mp4
• file:2-使用labelme进行数据与标签标注.mp4
• file:3-完成训练数据准备工作.mp4
• file:1-Labelme工具安装.mp4
• file:1-COCO数据集与人体姿态识别简介.mp4
• file:论文解读-1.mp4
• file:三代算法-3-faster-rcnn概述.mp4
• file:三代算法-2-深度学习经典检测方法.mp4
• file:5-RPN层的作用与实现解读.mp4
• file:4-基于不同尺度特征图生成所有框.mp4
• file:7-Proposal层实现方法.mp4
• file:9-正负样本选择与标签定义.mp4
• file:8-DetectionTarget层的作用.mp4
• file:10-RoiPooling层的作用与目的.mp4
• file:11-RorAlign操作的效果.mp4
• file:12-整体框架回顾.mp4
• file:5-tesseract-ocr安装配置.mp4
• file:6-文档扫描识别效果.mp4
• file:3-原始与变换坐标计算.mp4
• file:2-环境配置与预处理.mp4
• file:8-基于视频的车位检测.mp4
• file:6-车位区域划分.mp4
• file:d2l-en-pytorch.pdf
• file:动手学深度学习（2020.07.08）.zip
• file:深度学习之PyTorch物体检测实战.epub
• file:源代码.zip
• file:Python基础教程（第3版）高清英文版.pdf
• file:《跟着迪哥学 Python数据分析与机器学习实战》PDF+唐宇迪.pdf
• file:配套代码和数据集《跟着迪哥学 Python数据分析与机器学习实战》.zip
• file:7-得到线性回归方程.mp4
• file:QQ截图20190624141428.png
• file:1.png
• file:聚类算法-实验.zip
• file:模型评估方法.ipynb
• file:Kmeans-代码实现.zip
• file:Learning Combinatorial Optimization Algorithms over Graphs.pdf
• file:Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search(1).pdf
• file:Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs.pdf
• file:Dynamic Graph Generation Network- Generating Relational Knowledge from Diagrams.pdf
• file:Deep Reasoning with Knowledge Graph for Social Relationship Understanding.pdf
• file:Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering.pdf
• file:The More You Know- Using Knowledge Graphs for Image Classification.pdf
• file:Representation learning for visual-relational knowledge graphs.pdf
• file:Multi-Label Zero-Shot Learning with Structured Knowledge Graphs.pdf
• file:Knowledge Transfer for Out-of-Knowledge-Base Entities - A Graph Neural Network Approach.pdf
• file:Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.pdf
• file:N-ary relation extraction using graph state LSTM.pdf
• file:End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.pdf
• file:Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling.pdf
• file:Jointly Multiple Events Extraction via Attention-based Graph.pdf
• file:A Graph-to-Sequence Model for AMR-to-Text Generation.pdf
• file:Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.pdf
• file:Graph Convolutional Networks for Text Classification.pdf
• file:Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.pdf
• file:Graph Convolutional Networks with Argument-Aware Pooling for Event Detection.pdf
• file:Recurrent Relational Networks.pdf
• file:Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks..pdf
• file:Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.pdf
• file:NetGAN- Generating Graphs via Random Walks(1).pdf
• file:MolGAN- An implicit generative model for small molecular graphs(1).pdf
• file:Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.pdf
• file:Learning Graphical State Transitions.pdf
• file:Symbolic Graph Reasoning Meets Convolutions.pdf
• file:Learning Conditioned Graph Structures for Interpretable Visual Question Answering.pdf
• file:A Compositional Object-Based Approach to Learning Physical Dynamics.pdf
• file:Attend, Infer, Repeat- Fast Scene Understanding with Generative Models.pdf
• file:Self-Attention with Relative Position Representations.pdf
• file:Graph networks as learnable physics engines for inference and control.pdf
• file:Conversation Modeling on Reddit using a Graph-Structured LSTM.pdf
• file:Learning Deep Generative Models of Graphs.pdf
• file:A simple neural network module for relational reasoning.pdf
• file:VAIN- Attentional Multi-agent Predictive Modeling.pdf
• file:Semi-supervised User Geolocation via Graph Convolutional Networks.pdf
• file:Situation Recognition with Graph Neural Networks.pdf
• file:Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.pdf
• file:Interaction Networks for Learning about Objects, Relations and Physics.pdf
• file:Inference in Probabilistic Graphical Models by Graph Neural Networks.pdf
• file:Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.pdf
• file:Learning to Represent Programs with Graphs.pdf
• file:Translating Embeddings for Modeling Multi-relational Data.pdf
• file:Effective Approaches to Attention-based Neural Machine Translation.pdf
• file:NerveNet Learning Structured Policy with Graph Neural Networks.pdf
• file:Learning Multiagent Communication with Backpropagation.pdf
• file:Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf
• file:Learning a SAT Solver from Single-Bit Supervision.pdf
• file:Discovering objects and their relations from entangled scene representations.pdf
• file:Visual Interaction Networks- Learning a Physics Simulator from Vide.o.pdf
• file:Attention, Learn to Solve Routing Problems!.pdf
• file:Traffic Graph Convolutional Recurrent Neural Network- A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.pdf
• file:Graphical-Based Learning Environments for Pattern Recognition.pdf
• file:Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction.pdf
• file:Hierarchical Graph Representation Learning with Differentiable Pooling.pdf
• file:Neural networks for relational learning- an experimental comparison.pdf
• file:Covariant Compositional Networks For Learning Graphs.pdf
• file:Mean-field theory of graph neural networks in graph partitioning.pdf
• file:Graph Capsule Convolutional Neural Networks.pdf
• file:How Powerful are Graph Neural Networks-.pdf
• file:Graph Neural Networks for Ranking Web Pages.pdf
• file:CelebrityNet- A Social Network Constructed from Large-Scale Online Celebrity Images.pdf
• file:Geometric deep learning on graphs and manifolds using mixture model cnns.pdf
• file:Deriving Neural Architectures from Sequence and Graph Kernels.pdf
• file:Deep Sets.pdf
• file:A new model for learning in graph domains.pdf
• file:Contextual Graph Markov Model- A Deep and Generative Approach to Graph Processing.pdf
• file:A Comparison between Recursive Neural Networks and Graph Neural Networks.pdf
• file:Relational Inductive Biases, Deep Learning, and Graph Networks.pdf
• file:Graph Neural Networks：A Review of Methods and Applications.pdf
• file:The Graph Neural Network Model.pdf
• file:Neural Message Passing for Quantum Chemistry.pdf
• file:Geometric Deep Learning- Going beyond Euclidean data.pdf
• file:Deep Learning on Graphs- A Survey.pdf
• file:pycharm-community-2019.1.1.exe
• file:cuda_10.0.130_411.31_win10.exe
• file:cudnn-10.0-windows10-x64-v7.5.0.56 (1).zip
• file:Anaconda3-2019.03-Windows-x86_64.exe
• file:cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb
• file:7-加载训练好的权重.mp4
• file:8-迁移学习效果对比.mp4
• file:6-shortcut模块.mp4
• file:GitHub地址.txt
• file:MLY-zh-cn.pdf
• file:9-训练多分类模型.mp4
• file:8-整体流程debug解读.mp4
• file:2-递归生成树节点.mp4
• file:3-树模型预剪枝参数作用.mp4
• file:11-样本数量对结果的影响.mp4
• file:7-MiniBatch方法.mp4
• file:13-岭回归与lasso.mp4
• file:Kmeans算法模块概述.mp4
• file:10-半监督学习_20190805_232033.mp4
• file:9-应用实例-图像分割.mp4
• file:6-如何找到合适的K值.mp4
• file:5-评估指标-Inertia.mp4
• file:8-Kmenas算法存在的问题_20190805_232023.mp4
• file:mnist-original.mat
• file:PointNet- Deep Learning on Point Sets for 3D Classification and Segmentation.pdf
• file:Dynamic Graph CNN for Learning on Point Clouds.pdf
• file:Modeling polypharmacy side effects with graph convolutional networks.pdf
• file:Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs.pdf
• file:Out of the Box- Reasoning with Graph Convolution Nets for Factual Visual Question Answering(1).pdf
• file:Graph-Structured Representations for Visual Question Answering.pdf
• file:Relation Networks for Object Detection.pdf
• file:Structural-RNN- Deep Learning on Spatio-Temporal Graphs.pdf
• file:Bayesian Semi-supervised Learning with Graph Gaussian Processes.pdf
• file:Deep Convolutional Networks on Graph-Structured Data.pdf
• file:Spectral Networks and Deep Locally Connected.pdf
• file:Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.pdf
• file:Sentence-State LSTM for Text Representation.pdf
• file:Gated Graph Sequence Neural Networks.pdf
• file:Attention Is All You Need.pdf
• file:Graph Classification using Structural Attention.pdf
• file:Adaptive Sampling Towards Fast Graph Representation Learning.pdf
• file:Inductive Representation Learning on Large Graphs.pdf
• file:Stochastic Training of Graph Convolutional Networks with Variance Reduction.pdf
• file:Graph-to-Sequence Learning using Gated Graph Neural Networks.pdf
• file:Rethinking Knowledge Graph Propagation for Zero-Shot Learning.pdf
• folder:AI精选付费资料包（37.4GB）
• folder:六：计算机视觉实战项目
• folder:二：AI必读经典书籍
• folder:三：超详细人工智能学习大纲
• folder:四：机器学习基础算法教程
• folder:一：人工智能论文合集
• folder:五：深度学习神经网络基础教程
• folder:03.MASK-RCNN目标检测实战视频课程
• folder:01.OpenCV图像处理实战视频课程
• folder:06.YOLOV5目标检测课程资料
• folder:08.Unet图像分割课程资料
• folder:01.机器学习经典算法精讲视频课程
• folder:图神经网络（GNN）100篇论文集
• folder:CVPR行人重识别论文解读
• folder:ICCV2021
• folder:Resnet论文解读
• folder:CNN_不能错过的10篇论文
• folder:深度学习论文精讲-BERT模型
• folder:神经网络模型基础课件资料
• folder:GAN对抗生成网络基础
• folder:第一章：物体检测框架-MaskRcnn项目介绍与配置
• folder:第六章：必备基础-物体检测FasterRcnn系列
• folder:项目实战二：文档扫描OCR识别
• folder:项目实战三：全景图像拼接
• folder:项目实战五：答题卡识别判卷
• folder:03.深度学习相关书籍
• folder:01.Python基础书籍
• folder:第六章：逻辑回归代码实现
• folder:第十三章：决策树实验分析
• folder:第三章：模型评估方法
• folder:课程简介
• folder:第八章：聚类算法-Kmeans&Dbscan原理
• folder:部分代码资料
• folder:Applications
• folder:Models
• folder:Survey
• folder:解压密码： iccv2021
• folder:CNN+RNN+GAN
• folder:第五章：必备基础-迁移学习与Resnet网络架构
• folder:2-开源项目数据集
• folder:3-参数配置
• folder:4-maskrcnn源码修改方法
• folder:6-测试与展示模块
• folder:3-流程与结果演示
• folder:2-网络架构概述
• folder:4-论文解读-1-论文整体概述
• folder:1-三代算法-1-物体检测概述
• folder:6-候选框过滤方法
• folder:1-FPN层特征提取原理解读
• folder:4-透视变换结果
• folder:2-文档轮廓提取
• folder:2-RANSAC算法
• folder:5-模板匹配得出识别结果
• folder:4-输入数据处理方法
• folder:4-车位直线检测
• folder:5-按列划分区域
• folder:1-任务整体流程
• folder:7-识别模型构建
• folder:2-预处理操作
• folder:4-选项判断识别
• folder:21年最新-李沐《动手学深度学习第二版》中、英文版免费分享
• folder:《Python基础教程（第3版）》
• folder:吴恩达《Machine Learning Yearning》完整中文版
• folder:9-聚类算法实验分析
• folder:13-集成算法原理
• folder:11-决策树代码实现
• folder:1-线性回归原理推导
• folder:8-Kmeans代码实现
• folder:combinatorial optimization
• folder:knowledge graph
• folder:graph generation
• folder:science
• folder:propagation_type
• folder:training methods
• folder:课程安装软件-Win10
• folder:2-迁移学习策略
• folder:吴恩达MLY
• folder:10-准备测试数据
• folder:5-迭代优化参数
• folder:12-非线性决策边界
• folder:1-多分类逻辑回归整体思路
• folder:2-训练模块功能
• folder:4-优化目标定义
• folder:8-鸢尾花数据集多分类任务
• folder:9-多特征回归模型
• folder:5-数据集切分
• folder:3-整体框架逻辑
• folder:1-树模型可视化展示
• folder:2-决策边界展示分析
• folder:6-随机梯度下降得到的效果
• folder:2-参数直接求解方法
• folder:5-学习率对结果的影响
• folder:14-实验总结
• folder:8-不同策略效果对比
• folder:3-样本点归属划分
• folder:4-算法迭代更新
• folder:2-计算得到簇中心点
• folder:6-评估指标对比分析
• folder:1-Sklearn工具包简介
• folder:8-ROC曲线
• folder:3-交叉验证的作用
• folder:3-建模流程解读
• folder:1-Kmenas算法常用操作
• folder:Semantic Segmentation
• folder:Visual Question Answering
• folder:Region Classification
• folder:Object Detection
• folder:convolution
• folder:neighborhood sampling
• folder:receptive field control
• folder:edge-informative graph
• folder:Detection-PyTorch-Notebook
• folder:chapter6