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AI精选付费资料包（37.4GB）（合集）

  • 发布时间：
    2025-03-17 00:20:12
  • 文件大小：
    共计 214 个文件，合计：3.96GB
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资源列表
AI精选付费资料包（37.4GB）（合集）。 ├── 一：人工智能论文合集 ├── 二：AI必读经典书籍 ├── 三：超详细人工智能学习大纲 ├── 四：机器学习基础算法教程 ├── 五：深度学习神经网络基础教程 ├── 六：计算机视觉实战项目
📢 以下资源由夸克网盘用户[小女*本姓]于2024-10-17分享（文件数量过多时只展示部分文件）
AI精选付费资料包（37.4GB）（合集）3.96GB
一：人工智能论文合集846.74MB
CNN_不能错过的10篇论文65.26MB
1311.2524v5_R_CNN.pdf6.23MB
1311.2901v3_Visualizing and Understanding Convolutional Networks.pdf34.56MB
1406.2661v1_Generative Adversarial Nets.pdf518.05KB
1409.1556v6_VERY DEEP CONVOLUTIONAL Networks.pdf195.32KB
1412.2306v2_Deep Visual-Semantic Alignments for Generating Image Descriptions.pdf5.21MB
1504.08083_Fast R-CNN.pdf713.99KB
1506.01497v3_Faster R-CNN.pdf6.59MB
1506.02025_Spatial Transformer Networks.pdf7.89MB
1512.03385v1_Deep Residual Learning for Image Recognition.pdf800.18KB
4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf1.35MB
Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf1.24MB
cvpr20210B
解压密码：cvpr20210B
CVPR行人重识别论文解读109.24MB
1. 1-关键点位置特征构建.mp417.96MB
2. 2-图卷积与匹配的作用.mp420.81MB
4. 3-局部特征热度图计算.mp421.11MB
5. 4-基于图卷积构建人体拓扑关系.mp425.86MB
6. 5-图卷积模块实现方法.mp423.49MB
ICCV20210B
解压密码： iccv20210B
Resnet论文解读117.38MB
13-额外补充-Resnet论文解读.mp4117.38MB
深度学习论文精讲-BERT模型323.83MB
1. 课程介绍.mp436.9MB
2. 1-论文讲解思路概述.mp414.77MB
3. 2-BERT模型摘要概述.mp432.28MB
4. 3-模型在NLP领域应用效果.mp433.56MB
5. 4-预训练模型的作用.mp418.43MB
6. 5-输入数据特殊编码字符解析.mp443.88MB
7. 6-向量特征编码方法.mp424.65MB
8. 7-BERT模型训练策略.mp442.95MB
9. 8-论文总结分析.mp476.41MB
图神经网络（GNN）100篇论文集231.03MB
Applications161.39MB
combinatorial optimization3.44MB
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search(1).pdf537.04KB
Learning Combinatorial Optimization Algorithms over Graphs.pdf2.91MB
graph generation3.28MB
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.pdf517.97KB
MolGAN- An implicit generative model for small molecular graphs(1).pdf1.1MB
NetGAN- Generating Graphs via Random Walks(1).pdf1.67MB
image0B
Image classification0B
Interaction Detection0B
Object Detection0B
Region Classification0B
Semantic Segmentation0B
Visual Question Answering0B
knowledge graph17.35MB
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.pdf432.63KB
Deep Reasoning with Knowledge Graph for Social Relationship Understanding.pdf2.76MB
Dynamic Graph Generation Network- Generating Relational Knowledge from Diagrams.pdf1.19MB
Knowledge Transfer for Out-of-Knowledge-Base Entities - A Graph Neural Network Approach.pdf355.22KB
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering.pdf437.8KB
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs.pdf1.36MB
Representation learning for visual-relational knowledge graphs.pdf6.9MB
The More You Know- Using Knowledge Graphs for Image Classification.pdf2.31MB
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs.pdf1.63MB
science130.64MB
A Compositional Object-Based Approach to Learning Physical Dynamics.pdf4.26MB
A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.pdf340.4KB
A simple neural network module for relational reasoning.pdf1.37MB
Action Schema Networks- Generalised Policies with Deep Learning.pdf1.67MB
Adversarial Attack on Graph Structured Data.pdf593.12KB
Attend, Infer, Repeat- Fast Scene Understanding with Generative Models.pdf1.3MB
Attention, Learn to Solve Routing Problems!.pdf1.48MB
Beyond Categories- The Visual Memex Model for Reasoning About Object Relationships.pdf618.71KB
Combining Neural Networks with Personalized PageRank for Classification on Graphs.pdf483.25KB
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.pdf567.14KB
Constructing Narrative Event Evolutionary Graph for Script Event Prediction.pdf654.87KB
Conversation Modeling on Reddit using a Graph-Structured LSTM.pdf682.35KB
Convolutional networks on graphs for learning molecular fingerprints.pdf785.36KB
Cross-Sentence N-ary Relation Extraction with Graph LSTMs.pdf540.89KB
Deep Graph Infomax.pdf8.15MB
DeepInf- Modeling influence locality in large social networks.pdf1.07MB
Discovering objects and their relations from entangled scene representations.pdf4.99MB
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.pdf567.07KB
Effective Approaches to Attention-based Neural Machine Translation.pdf243.97KB
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks.pdf6.99MB
Graph Convolutional Matrix Completion.pdf732.99KB
Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf9.84MB
Graph networks as learnable physics engines for inference and control.pdf2.72MB
GraphRNN- Generating Realistic Graphs with Deep Auto-regressive Models.pdf2.43MB
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification.pdf2.52MB
Hyperbolic Attention Networks.pdf3.08MB
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.pdf304.16KB
Inference in Probabilistic Graphical Models by Graph Neural Networks.pdf3.07MB
Interaction Networks for Learning about Objects, Relations and Physics.pdf1.91MB
Learning a SAT Solver from Single-Bit Supervision.pdf1.89MB
Learning Conditioned Graph Structures for Interpretable Visual Question Answering.pdf8.48MB
Learning Deep Generative Models of Graphs.pdf2.31MB
Learning Graphical State Transitions.pdf1.47MB
Learning Human-Object Interactions by Graph Parsing Neural Networks.pdf3.91MB
Learning model-based planning from scratch.pdf1.28MB
Learning Multiagent Communication with Backpropagation.pdf4.13MB
Learning to Represent Programs with Graphs.pdf421.9KB
Metacontrol for Adaptive Imagination-Based Optimization.pdf1.6MB
Molecular Graph Convolutions- Moving Beyond Fingerprints.pdf2.08MB
NerveNet Learning Structured Policy with Graph Neural Networks.pdf3.11MB
Neural Combinatorial Optimization with Reinforcement Learning.pdf393.17KB
Neural Module Networks.pdf1.03MB
Neural Relational Inference for Interacting Systems.pdf2.83MB
Protein Interface Prediction using Graph Convolutional Networks.pdf837.75KB
Relational Deep Reinforcement Learning.pdf6.81MB
Relational inductive bias for physical construction in humans and machines.pdf1022.51KB
Relational neural expectation maximization- Unsupervised discovery of objects and their interactions.pdf1.15MB
Self-Attention with Relative Position Representations.pdf229.86KB
Semi-supervised User Geolocation via Graph Convolutional Networks.pdf1.13MB
Situation Recognition with Graph Neural Networks.pdf5.27MB
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.pdf1.5MB
Spatio-Temporal Graph Convolutional Networks- A Deep Learning Framework for Traffic Forecasting.pdf895.05KB
Structured Dialogue Policy with Graph Neural Networks.pdf779.24KB
Symbolic Graph Reasoning Meets Convolutions.pdf3.23MB
Traffic Graph Convolutional Recurrent Neural Network- A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.pdf1.45MB
Translating Embeddings for Modeling Multi-relational Data.pdf414.17KB
Understanding Kin Relationships in a Photo.pdf1.44MB
VAIN- Attentional Multi-agent Predictive Modeling.pdf423.97KB
Visual Interaction Networks- Learning a Physics Simulator from Vide.o.pdf5.41MB
text6.7MB
A Graph-to-Sequence Model for AMR-to-Text Generation.pdf290.15KB
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling.pdf621.87KB
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.pdf363.06KB
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.pdf604.59KB
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks..pdf453.5KB
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.pdf784.41KB
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.pdf346.9KB
Graph Convolutional Networks for Text Classification.pdf1.83MB
Graph Convolutional Networks with Argument-Aware Pooling for Event Detection.pdf324.7KB
Jointly Multiple Events Extraction via Attention-based Graph.pdf430.38KB
N-ary relation extraction using graph state LSTM.pdf455.67KB
Recurrent Relational Networks.pdf307KB
Models44.65MB
graph_type5.65MB
directed graph0B
edge-informative graph0B
Adaptive Graph Convolutional Neural Networks.pdf803.92KB
Graph Capsule Convolutional Neural Networks.pdf1.93MB
Graph Neural Networks for Object Localization.pdf221.83KB
Graph Neural Networks for Ranking Web Pages.pdf1.01MB
Graph Partition Neural Networks for Semi-Supervised Classification.pdf713.9KB
How Powerful are Graph Neural Networks-.pdf678.3KB
Mean-field theory of graph neural networks in graph partitioning.pdf369.44KB
others30.67MB
A Comparison between Recursive Neural Networks and Graph Neural Networks.pdf247.2KB
A new model for learning in graph domains.pdf177.61KB
CelebrityNet- A Social Network Constructed from Large-Scale Online Celebrity Images.pdf16.33MB
Contextual Graph Markov Model- A Deep and Generative Approach to Graph Processing.pdf570.59KB
Deep Sets.pdf5.11MB
Deriving Neural Architectures from Sequence and Graph Kernels.pdf687.05KB
Diffusion-Convolutional Neural Networks.pdf366.35KB
Geometric deep learning on graphs and manifolds using mixture model cnns.pdf7.23MB
propagation_type0B
attention0B
convolution0B
gate0B
skip0B
training methods8.33MB
boosting0B
neighborhood sampling0B
receptive field control0B
Covariant Compositional Networks For Learning Graphs.pdf482.53KB
Graphical-Based Learning Environments for Pattern Recognition.pdf335.92KB
Hierarchical Graph Representation Learning with Differentiable Pooling.pdf2.31MB
Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction.pdf1000.46KB
Learning Steady-States of Iterative Algorithms over Graphs.pdf3.09MB
Neural networks for relational learning- an experimental comparison.pdf1.15MB
Survey24.96MB
一般推荐10.63MB
A Comprehensive Survey on Graph Neural Networks.pdf1.8MB
Computational Capabilities of Graph Neural Networks(1).pdf1.28MB
Deep Learning on Graphs- A Survey.pdf1.8MB
Geometric Deep Learning- Going beyond Euclidean data.pdf5.26MB
Neural Message Passing for Quantum Chemistry.pdf511.15KB
极力推荐14.33MB
Graph Neural Networks：A Review of Methods and Applications.pdf2.67MB
Non-local Neural Networks.pdf1.24MB
Relational Inductive Biases, Deep Learning, and Graph Networks.pdf8.99MB
The Graph Neural Network Model.pdf1.43MB
论文集索引.jpg29.73KB
二：AI必读经典书籍2.04GB
01.人工智能行业报告129.49MB
53份人工智能行业报告.zip129.49MB
02.AI必读经典书籍1.92GB
01.Python基础书籍6.05MB
《Python基础教程（第3版）》6.05MB
Python基础教程（第3版）高清英文版.pdf5.96MB
源代码.zip87.95KB
02.机器学习相关书籍502.59MB
《跟着迪哥学 Python数据分析与机器学习实战》206.23MB
《跟着迪哥学 Python数据分析与机器学习实战》.epub40.11MB
《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》PDF+唐宇迪.pdf98.83MB
吴恩达《Machine Learning Yearning》完整中文版0B
吴恩达MLY0B
机器学习〔中文版〕.pdf9.91MB
机器学习_周志华.pdf37.53MB
机器学习导论 原书 第2版.pdf77.76MB
机器学习个人笔记完整版2.5.pdf7.75MB
机器学习实践指南++案例应用解析+麦好.pdf59.27MB
机器学习实战.pdf13.41MB
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
图解机器学习.pdf59.4MB
凸优化.pdf5.73MB
03.深度学习相关书籍339.32MB
《深度学习之PyTorch物体检测实战》PDF+源代码34.73MB
源代码0B
深度学习之PyTorch物体检测实战.epub10.35MB
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.pdf11.64MB
深度学习之PyTorch物体检测实战论文导引.docx30.41KB
21年最新-李沐《动手学深度学习第二版》中、英文版免费分享78.58MB
d2l-en-pytorch.pdf26.97MB
d2l-zh-pytorch.pdf18.1MB
Dive-into-DL-Pytorch.pdf33.5MB
《神经网络与深度学习》(邱锡鹏-20191121).pdf7.02MB
《TensorFlow 2.0深度学习算法实战教材》-中文版教材分享.pdf21.41MB
深度学习(花园书).pdf32.99MB
深度学习技术图像处理入门 by 杨培文，胡博强 ().pdf125.1MB
Tensorflow技术解析与实战.pdf39.49MB
04.计算机视觉相关书籍1.03GB
超详细的计算机视觉书籍.zip1.03GB
OpenCV书籍.rar63.15MB
三：超详细人工智能学习大纲20.32MB
人工智能大纲升级版本.pdf20.32MB
四：机器学习基础算法教程1.07GB
01.机器学习经典算法精讲视频课程1.07GB
第一章：线性回归原理推导237.81MB
0-课程简介.mp434.95MB
1-回归问题概述.mp419.65MB
2-误差项定义.mp426.5MB
3-独立同分布的意义.mp424.48MB
4-似然函数的作用.mp429.04MB
5-参数求解.mp430.74MB
6-梯度下降通俗解释.mp420.79MB
7参数更新方法.mp424.87MB
8-优化参数设置.mp426.8MB
第二章：线性回归代码实现113.98MB
第一章：线性回归113.98MB
4-损失与预测模块0B
5-数据与标签定义0B
6-训练线性回归模型0B
8-整体流程debug解读0B
9-多特征回归模型0B
10-非线性回归0B
1-线性回归整体模块概述.mp414.46MB
2-初始化步骤.mp424.11MB
3-实现梯度下降优化模块.mp439.6MB
7-得到线性回归方程.mp435.82MB
第三章：模型评估方法0B
分类模型评估0B
1-Sklearn工具包简介0B
2-数据集切分0B
3-交叉验证的作用0B
4-交叉验证实验分析0B
5-混淆矩阵0B
6-评估指标对比分析0B
7-阈值对结果的影响0B
8-ROC曲线0B
第四章：线性回归实验分析20.5MB
线性回归20.5MB
2-参数直接求解方法0B
3-预处理对结果的影响0B
4-梯度下降模块0B
5-学习率对结果的影响0B
6-随机梯度下降得到的效果0B
7-MiniBatch方法0B
8-不同策略效果对比0B
9-多项式回归0B
10-模型复杂度0B
11-样本数量对结果的影响0B
12-正则化的作用0B
13-岭回归与lasso0B
14-实验总结0B
1-实验目标分析.mp420.5MB
第五章：逻辑回归原理推导52.45MB
1-逻辑回归算法原理.mp423MB
2-化简与求解.mp429.45MB
第六章：逻辑回归代码实现0B
第二章：逻辑回归0B
1-多分类逻辑回归整体思路0B
2-训练模块功能0B
3-完成预测模块0B
4-优化目标定义0B
5-迭代优化参数0B
6-梯度计算0B
7-得出最终结果0B
8-鸢尾花数据集多分类任务0B
9-训练多分类模型0B
10-准备测试数据0B
11-决策边界绘制0B
12-非线性决策边界0B
第七章：逻辑回归实验分析291.95MB
1-逻辑回归实验概述.mp452.15MB
2-概率结果随特征数值的变化.mp446.69MB
3-可视化展示.mp433.21MB
4-坐标棋盘制作.mp438.18MB
5-分类决策边界展示分析.mp461.13MB
6-多分类-softmax.mp460.57MB
第八章：聚类算法-Kmeans&Dbscan原理187.68MB
1-KMEANS算法概述.mp428.94MB
2-KMEANS工作流程.mp423.12MB
3-KMEANS迭代可视化展示.mp431.7MB
4-DBSCAN聚类算法.mp429.35MB
5-DBSCAN工作流程.mp441.61MB
6-DBSCAN可视化展示.mp432.97MB
第九章：Kmeans代码实现0B
第三章：聚类-Kmeans0B
1-Kmeans算法模块概述0B
2-计算得到簇中心点0B
3-样本点归属划分0B
4-算法迭代更新0B
5-鸢尾花数据集聚类任务0B
6-聚类效果展示0B
第十章：聚类算法实验分析0B
聚类0B
1-Kmenas算法常用操作0B
2-聚类结果展示0B
3-建模流程解读0B
4-不稳定结果0B
5-评估指标-Inertia0B
6-如何找到合适的K值0B
7-轮廓系数的作用0B
8-Kmenas算法存在的问题0B
9-应用实例-图像分割0B
10-半监督学习0B
11-DBSCAN算法0B
第十一章：决策树原理188.65MB
1-决策树算法概述.mp424.28MB
2-熵的作用.mp422.82MB
3-信息增益原理.mp430.3MB
4-决策树构造实例.mp425.13MB
5-信息增益率与gini系数.mp418.2MB
6-预剪枝方法.mp425.09MB
7-后剪枝方法.mp424.55MB
8-回归问题解决.mp418.27MB
第十二章：决策树代码实现0B
第五章：决策树0B
1-整体模块概述0B
2-递归生成树节点0B
3-整体框架逻辑0B
4-熵值计算0B
5-数据集切分0B
6-完成树模型构建0B
7-测试算法效果0B
第十三章：决策树实验分析0B
决策树0B
1-树模型可视化展示0B
2-决策边界展示分析0B
3-树模型预剪枝参数作用0B
4-回归树模型0B
课程简介809.9KB
项目截图798.61KB
1.png160.05KB
QQ截图20190624141129.png137.84KB
QQ截图20190624141231.png103.51KB
QQ截图20190624141330.png256.27KB
QQ截图20190624141428.png140.94KB
Python机器学习实训营.docx11.29KB
02.机器学习算法课件资料0B
部分代码资料0B
1-线性回归原理推导0B
2-线性回归代码实现0B
3-模型评估方法0B
3-线性回归实验分析0B
5-逻辑回归代码实现0B
6-逻辑回归实验分析0B
7-聚类算法-Kmeans&Dbscan原理0B
8-Kmeans代码实现0B
9-聚类算法实验分析0B
10-决策树原理0B
11-决策树代码实现0B
12-决策树实验分析0B
13-集成算法原理0B
14-集成算法实验分析0B
15-支持向量机原理推导0B
机器学习算法PPT0B
五：深度学习神经网络基础教程0B
六：计算机视觉实战项目0B
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