AI精选付费资料包37.4GB

类型：	课程

文件预览

资源不是实时更新，具体以网盘链接为准！

点我预览网盘文件内容

AI精选付费资料包37.4GB

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 (129.5 MB)

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 (5.96 MB)

源代码.zip (88.0 KB)

02.机器学习相关书籍

吴恩达《Machine Learning Yearning》完整中文版

吴恩达MLY

MLY-zh-cn.pdf (5.00 MB)

《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》.epub (40.1 MB)

《跟着迪哥学 Python数据分析与机器学习实战》.mobi (67.3 MB)

《跟着迪哥学 Python数据分析与机器学习实战》PDF 唐宇迪.pdf (98.8 MB)

机器学习_周志华.pdf (37.5 MB)

机器学习导论原书第2版.pdf (77.8 MB)

机器学习个人笔记完整版2.5.pdf (7.75 MB)

机器学习实践指南案例应用解析麦好.pdf (59.3 MB)

机器学习实战.pdf (13.4 MB)

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 (25.6 MB)

机器学习〔中文版〕.pdf (9.91 MB)

图解机器学习.pdf (59.4 MB)

凸优化.pdf (5.73 MB)

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

d2l-en-pytorch.pdf (27.0 MB)

d2l-zh-pytorch.pdf (18.1 MB)

Dive-into-DL-Pytorch.pdf (33.5 MB)

《深度学习之PyTorch物体检测实战》PDF 源代码

源代码

Detection-PyTorch-Notebook

chapter1

model-evaluation

conf

arial.ttf (304.3 KB)

conf.yaml (459.0 B)

data

detections

1.txt (162.0 B)

groundtruths

1.txt (110.0 B)

results

class1.png (15.0 KB)

class2.png (15.0 KB)

lib

__pycache__

detection.cpython-36.pyc (2.86 KB)

Evaluator.cpython-36.pyc (3.85 KB)

utils.cpython-36.pyc (5.37 KB)

detection.py (3.64 KB)

detection.pyc (13.1 KB)

Evaluator.py (4.43 KB)

Evaluator.pyc (13.3 KB)

utils.py

utils.pyc

evaluation.ipynb (65.1 KB)

evaluation.py (464.0 B)

README.md (315.0 B)

chapter2

mlp.py (437.0 B)

perception.py (732.0 B)

perception_sequential.py (375.0 B)

visdom.py (366.0 B)

chapter3

densenet_block.py (1.11 KB)

detnet_bottleneck.py (1.13 KB)

fpn.py (3.15 KB)

inceptionv1.py (1.30 KB)

inceptionv2.py (1.25 KB)

resnet_bottleneck.py (966.0 B)

vgg.py (938.0 B)

chapter4

faster-rcnn-pytorch

cfgs

res50.yml (347.0 B)

res101.yml (363.0 B)

res101_ls.yml (439.0 B)

vgg16.yml (287.0 B)

images

img1.jpg (76.9 KB)

img1_det.jpg (83.8 KB)

img1_det_res101.jpg (83.8 KB)

img2.jpg (110.6 KB)

img2_det.jpg (111.4 KB)

img2_det_res101.jpg (111.4 KB)

img3.jpg (100.4 KB)

img3_det.jpg (104.9 KB)

img3_det_res101.jpg (104.9 KB)

img4.jpg (83.0 KB)

img4_det.jpg (89.3 KB)

img4_det_res101.jpg (89.3 KB)

lib

datasets

tools

mcg_munge.py (1.46 KB)

VOCdevkit-matlab-wrapper

get_voc_opts.m (231.0 B)

voc_eval.m (1.30 KB)

xVOCap.m (258.0 B)

__init__.py (248.0 B)

coco.py (11.8 KB)

ds_utils.py (1.37 KB)

factory.py (2.61 KB)

imagenet.py (8.22 KB)

imdb.py (8.90 KB)

pascal_voc.py (14.7 KB)

pascal_voc_rbg.py (11.0 KB)

vg.py (16.4 KB)

vg_eval.py (4.08 KB)

voc_eval.py (6.50 KB)

model

faster_rcnn

__init__.py

faster_rcnn.py (5.65 KB)

resnet.py (8.58 KB)

vgg16.py (2.06 KB)

nms

_ext

nms

__init__.py (377.0 B)

__init__.py

src

nms_cuda.h (272.0 B)

nms_cuda_kernel.cu (5.49 KB)

nms_cuda_kernel.h (206.0 B)

.gitignore (15.0 B)

__init__.py

build.py (850.0 B)

make.sh (209.0 B)

nms_cpu.py (862.0 B)

nms_gpu.py (299.0 B)

nms_kernel.cu (4.95 KB)

nms_wrapper.py (757.0 B)

roi_align

_ext

roi_align

__init__.py (383.0 B)

__init__.py

functions

__init__.py

roi_align.py (1.96 KB)

modules

__init__.py

roi_align.py (1.63 KB)

src

roi_align.c (7.39 KB)

roi_align.h (361.0 B)

roi_align_cuda.c (2.37 KB)

roi_align_cuda.h (369.0 B)

roi_align_kernel.cu (7.55 KB)

roi_align_kernel.h (1.23 KB)

__init__.py

build.py (902.0 B)

make.sh (211.0 B)

roi_crop

_ext

crop_resize

__init__.py (310.0 B)

roi_crop

__init__.py (382.0 B)

__init__.py

functions

__init__.py

crop_resize.py (1.51 KB)

gridgen.py (2.18 KB)

roi_crop.py (1,002.0 B)

modules

__init__.py

gridgen.py (16.1 KB)

roi_crop.py (287.0 B)

src

roi_crop.c (22.6 KB)

roi_crop.h (659.0 B)

roi_crop_cuda.c (4.60 KB)

roi_crop_cuda.h (481.0 B)

roi_crop_cuda_kernel.cu (16.8 KB)

roi_crop_cuda_kernel.h (2.75 KB)

__init__.py

build.py (881.0 B)

make.sh (219.0 B)

roi_pooling

_ext

roi_pooling

__init__.py (385.0 B)

__init__.py

functions

__init__.py

roi_pool.py (1.73 KB)

modules

__init__.py

roi_pool.py (524.0 B)

src

roi_pooling.c (4.01 KB)

roi_pooling.h (178.0 B)

roi_pooling_cuda.c (2.77 KB)

roi_pooling_cuda.h (420.0 B)

roi_pooling_kernel.cu (9.35 KB)

roi_pooling_kernel.h (767.0 B)

__init__.py

build.py (875.0 B)

rpn

__init__.py

anchor_target_layer.py (8.79 KB)

bbox_transform.py (9.07 KB)

generate_anchors.py (3.17 KB)

proposal_layer.py (6.87 KB)

proposal_target_layer_cascade.py (9.10 KB)

rpn.py (4.19 KB)

utils

.gitignore (15.0 B)

__init__.py

bbox.pyx (3.35 KB)

blob.py (1.60 KB)

config.py (11.5 KB)

logger.py (2.41 KB)

net_utils.py (7.32 KB)

__init__.py

pycocotools

__init__.py (21.0 B)

_mask.pyx (10.5 KB)

coco.py (14.7 KB)

cocoeval.py (19.4 KB)

license.txt (1.50 KB)

mask.py (3.95 KB)

maskApi.c (7.52 KB)

maskApi.h (1.88 KB)

UPSTREAM_REV (80.0 B)

roi_data_layer

__init__.py (248.0 B)

minibatch.py (2.85 KB)

roibatchLoader.py (8.59 KB)

roidb.py (4.00 KB)

make.sh (1.25 KB)

setup.py (4.69 KB)

logs

vgg_voc

events.out.tfevents.1541645707.aizz (25.0 B)

events.out.tfevents.1541645748.aizz (25.0 B)

events.out.tfevents.1541645839.aizz (25.0 B)

events.out.tfevents.1541646048.aizz (1.24 MB)

events.out.tfevents.1542006392.aizz (25.0 B)

events.out.tfevents.1542007135.aizz (25.0 B)

events.out.tfevents.1542007423.aizz (25.0 B)

events.out.tfevents.1542007525.aizz (25.0 B)

events.out.tfevents.1542007598.aizz (25.0 B)

events.out.tfevents.1542007867.aizz (567.0 B)

events.out.tfevents.1542983031.aizz (291.0 B)

.gitignore (2.82 KB)

_init_paths.py (312.0 B)

demo.py (13.4 KB)

LICENSE (1.04 KB)

README.md (6.86 KB)

requirements.txt (80.0 B)

test_net.py (11.9 KB)

trainval_net.py (14.7 KB)

chapter5

dssd-pytorch

arm.py (764.0 B)

tcb.py (722.0 B)

ssd-pytorch

.idea

encodings.xml (135.0 B)

misc.xml (185.0 B)

modules.xml (288.0 B)

ssd.pytorch-master.iml (398.0 B)

vcs.xml (183.0 B)

workspace.xml (21.1 KB)

__pycache__

ssd.cpython-35.pyc (6.65 KB)

data

__pycache__

__init__.cpython-35.pyc (1.82 KB)

coco.cpython-35.pyc (7.78 KB)

config.cpython-35.pyc (948.0 B)

voc0712.cpython-35.pyc (6.82 KB)

scripts

COCO2014.sh (1.91 KB)

VOC2007.sh (971.0 B)

VOC2012.sh (763.0 B)

__init__.py (1.31 KB)

config.py (726.0 B)

example.jpg (136.8 KB)

voc0712.py (6.40 KB)

demo

__init__.py

demo.ipynb (1.26 MB)

live.py (3.00 KB)

doc

detection_example.png (365.2 KB)

detection_example2.png (318.8 KB)

detection_examples.png (1.96 MB)

SSD.jpg (47.2 KB)

ssd.png (71.0 KB)

layers

__pycache__

__init__.cpython-35.pyc (175.0 B)

box_utils.cpython-35.pyc (8.37 KB)

functions

__pycache__

__init__.cpython-35.pyc (259.0 B)

detection.cpython-35.pyc (2.51 KB)

prior_box.cpython-35.pyc (1.91 KB)

__init__.py (97.0 B)

detection.py (2.63 KB)

prior_box.py (1.95 KB)

modules

__pycache__

__init__.cpython-35.pyc (262.0 B)

l2norm.cpython-35.pyc (1.30 KB)

multibox_loss.cpython-35.pyc (4.18 KB)

__init__.py (105.0 B)

l2norm.py (758.0 B)

multibox_loss.py (5.82 KB)

__init__.py (48.0 B)

box_utils.py (9.61 KB)

utils

__pycache__

__init__.cpython-35.pyc (182.0 B)

augmentations.cpython-35.pyc (15.7 KB)

__init__.py (42.0 B)

augmentations.py (13.1 KB)

weights

vgg16_reducedfc.pth (78.1 MB)

.gitattributes (110.0 B)

.gitignore (1.42 KB)

eval.py (15.5 KB)

LICENSE (1.06 KB)

README.md (7.18 KB)

ssd.py (7.15 KB)

test.py (3.78 KB)

train.py (8.07 KB)

chapter6

yolov2-pytorch

cfgs

exps

__init__.py

darknet19_exp1.py (447.0 B)

darknet19_exp2.py (447.0 B)

__init__.py

config.py (2.70 KB)

config_voc.py (561.0 B)

datasets

__init__.py

imdb.py (5.02 KB)

pascal_voc.py (10.6 KB)

voc_eval.py (7.02 KB)

demo

out

2007_000039.jpg (66.5 KB)

dog.jpg (181.9 KB)

eagle.jpg (155.6 KB)

giraffe.jpg (231.7 KB)

horses.jpg (145.7 KB)

person.jpg (121.6 KB)

ragged-edge-london-office-6.jpg (1.40 MB)

scream.jpg (72.1 KB)

2007_000039.jpg (63.2 KB)

dog.jpg (159.9 KB)

eagle.jpg (138.6 KB)

giraffe.jpg (374.0 KB)

horses.jpg (130.4 KB)

person.jpg (111.2 KB)

ragged-edge-london-office-6.jpg (595.1 KB)

scream.jpg (170.4 KB)

layers

reorg

_ext

reorg_layer

__init__.py (385.0 B)

__init__.py

src

reorg_cpu.c (1.02 KB)

reorg_cpu.h (123.0 B)

reorg_cuda.c (453.0 B)

reorg_cuda.h (122.0 B)

reorg_cuda_kernel.cu (1.80 KB)

reorg_cuda_kernel.h (251.0 B)

__init__.py

build.py (802.0 B)

reorg_layer.py (1.59 KB)

roi_pooling

_ext

roi_pooling

__init__.py (385.0 B)

__init__.py

src

cuda

roi_pooling_kernel.cu (7.82 KB)

roi_pooling_kernel.h (767.0 B)

roi_pooling.c (4.01 KB)

roi_pooling.h (178.0 B)

roi_pooling_cuda.c (2.75 KB)

roi_pooling_cuda.h (420.0 B)

__init__.py

build.py (822.0 B)

roi_pool.py (3.17 KB)

roi_pool_py.py (2.21 KB)

__init__.py

utils

nms

.gitignore (15.0 B)

__init__.py

cpu_nms.pyx (2.19 KB)

gpu_nms.hpp (146.0 B)

gpu_nms.pyx (1.08 KB)

nms_kernel.cu (4.95 KB)

py_cpu_nms.py (1.03 KB)

pycocotools

__init__.py (21.0 B)

_mask.c (583.6 KB)

_mask.pyx (10.5 KB)

coco.py (15.1 KB)

cocoeval.py (20.3 KB)

license.txt (1.50 KB)

mask.py (3.96 KB)

maskApi.c (7.52 KB)

maskApi.h (1.88 KB)

UPSTREAM_REV (80.0 B)

__init__.py

bbox.c (449.8 KB)

bbox.pyx (9.24 KB)

build.py (6.00 KB)

im_transform.py (973.0 B)

network.py (4.31 KB)

nms_wrapper.py (866.0 B)

timer.py (1.08 KB)

yolo.c (290.8 KB)

yolo.py (7.31 KB)

yolo.pyx (1.69 KB)

darknet.py (12.0 KB)

demo.py (2.73 KB)

make.sh (488.0 B)

README.md (4.63 KB)

requirements.txt (64.0 B)

test.py (4.93 KB)

train.py (4.70 KB)

chapter7

mobilenet_v1.py (1.45 KB)

mobilenet_v2.py (4.11 KB)

mobilenet_v2_block.py (743.0 B)

shufflenet_v1.py (1.94 KB)

squeezenet_fire.py (978.0 B)

chapter8

nms.py (895.0 B)

retinanet.py (1.30 KB)

README.md (29.0 B)

GitHub地址.txt (57.0 B)

深度学习之PyTorch物体检测实战.epub (10.4 MB)

深度学习之PyTorch物体检测实战.mobi (12.7 MB)

深度学习之PyTorch物体检测实战.pdf (11.6 MB)

深度学习之PyTorch物体检测实战论文导引.docx (30.4 KB)

深度学习(花园书).pdf (33.0 MB)

深度学习技术图像处理入门 by 杨培文，胡博强 (z-lib.org).pdf (125.1 MB)

Tensorflow技术解析与实战.pdf (39.5 MB)

《神经网络与深度学习》(邱锡鹏-20191121).pdf (7.02 MB)

《TensorFlow 2.0深度学习算法实战教材》-中文版教材分享.pdf (21.4 MB)

04.计算机视觉相关书籍

超详细的计算机视觉书籍.zip (1.03 GB)

OpenCV书籍.rar (63.1 MB)

六：计算机视觉实战项目

01.OpenCV图像处理实战视频课程

项目实战二：文档扫描OCR识别

1-整体流程演示

1-整体流程演示.mp4 (21.2 MB)

2-文档轮廓提取

2-文档轮廓提取.mp4 (27.4 MB)

3-原始与变换坐标计算

3-原始与变换坐标计算.mp4 (25.8 MB)

4-透视变换结果

4-透视变换结果.mp4 (32.4 MB)

5-tesseract-ocr安装配置

5-tesseract-ocr安装配置.mp4 (40.9 MB)

6-文档扫描识别效果

6-文档扫描识别效果.mp4 (28.6 MB)

项目实战三：全景图像拼接

1-特征匹配方法

1-特征匹配方法.mp4 (28.1 MB)

2-RANSAC算法

2-RANSAC算法.mp4 (34.0 MB)

2-图像拼接方法

2-图像拼接方法.mp4 (44.5 MB)

4-流程解读

4-流程解读.mp4 (21.4 MB)

项目实战四：停车场车位识别

1-任务整体流程

1-任务整体流程.mp4 (71.0 MB)

2-所需数据介绍

2-所需数据介绍.mp4 (34.0 MB)

3-图像数据预处理

3-图像数据预处理.mp4 (56.3 MB)

4-车位直线检测

4-车位直线检测.mp4 (60.8 MB)

5-按列划分区域

5-按列划分区域.mp4 (54.1 MB)

6-车位区域划分

6-车位区域划分.mp4 (56.8 MB)

7-识别模型构建

7-识别模型构建.mp4 (40.9 MB)

8-基于视频的车位检测

8-基于视频的车位检测.mp4 (135.1 MB)

项目实战五：答题卡识别判卷

1-整体流程与效果概述

1-整体流程与效果概述.mp4 (29.1 MB)

2-预处理操作

2-预处理操作.mp4 (23.7 MB)

3-填涂轮廓检测

3-填涂轮廓检测.mp4 (25.3 MB)

4-选项判断识别

4-选项判断识别.mp4 (56.6 MB)

项目实战一：信用卡数字识别

1-总体流程与方法讲解

总体流程与方法讲解.mp4 (20.3 MB)

2-环境配置与预处理

2-环境配置与预处理.mp4 (34.4 MB)

3-模板处理方法

3-模板处理方法.mp4 (23.3 MB)

4-输入数据处理方法

4-输入数据处理方法.mp4 (28.4 MB)

5-模板匹配得出识别结果

5-模板匹配得出识别结果.mp4 (47.2 MB)

02.YOLOV5目标检测视频课程

1.任务需求与项目概述.mp4 (12.5 MB)

2-数据与标签配置方法.mp4 (28.4 MB)

3-标签转格式脚本制作.mp4 (23.8 MB)

4-各版本模型介绍.mp4 (24.3 MB)

5-项目参数配置.mp4 (18.9 MB)

6-缺陷检测模型培训.mp4 (27.2 MB)

7-输出结果与项目总结.mp4 (32.4 MB)

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

0-课程简介

0-课程简介.mp4 (18.6 MB)

1-Mask-Rcnn开源项目简介

0-Mask-Rcnn开源项目简介.mp4 (87.8 MB)

2-开源项目数据集

0-开源项目数据集.mp4 (42.2 MB)

3-参数配置

0-参数配置.mp4 (97.3 MB)

第五章：必备基础-迁移学习与Resnet网络架构

1-迁移学习的目标

1-迁移学习的目标.mp4 (11.5 MB)

2-迁移学习策略

2-迁移学习策略.mp4 (15.1 MB)

3-Resnet原理

3-Resnet原理.mp4 (107.3 MB)

4-Resnet网络细节

4-Resnet网络细节.mp4 (38.8 MB)

5-Resnet基本处理操作

5-Resnet基本处理操作.mp4 (31.4 MB)

6-shortcut模块

6-shortcut模块.mp4 (40.6 MB)

7-加载训练好的权重

7-加载训练好的权重.mp4 (38.0 MB)

8-迁移学习效果对比

8-迁移学习效果对比.mp4 (53.1 MB)

第二章：MaskRcnn网络框架源码详解

1-FPN层特征提取原理解读

1-FPN层特征提取原理解读.mp4 (41.6 MB)

2-FPN网络架构实现解读

2-FPN网络架构实现解读.mp4 (55.2 MB)

3-生成框比例设置

3-生成框比例设置.mp4 (27.9 MB)

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 (32.5 MB)

5-RPN层的作用与实现解读

5-RPN层的作用与实现解读.mp4 (30.4 MB)

6-候选框过滤方法

6-候选框过滤方法.mp4 (15.3 MB)

7-Proposal层实现方法

7-Proposal层实现方法.mp4 (32.9 MB)

8-DetectionTarget层的作用

8-DetectionTarget层的作用.mp4 (25.3 MB)

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 (27.3 MB)

10-RoiPooling层的作用与目的

10-RoiPooling层的作用与目的.mp4 (32.9 MB)

11-RorAlign操作的效果

11-RorAlign操作的效果.mp4 (25.3 MB)

12-整体框架回顾

12-整体框架回顾.mp4 (28.4 MB)

第三章：基于MASK-RCNN框架训练自己的数据与任务

1-Labelme工具安装

1-Labelme工具安装.mp4 (14.1 MB)

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 (25.8 MB)

3-完成训练数据准备工作

3-完成训练数据准备工作.mp4 (26.1 MB)

4-maskrcnn源码修改方法

4-maskrcnn源码修改方法.mp4 (63.0 MB)

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 (39.4 MB)

6-测试与展示模块

6-测试与展示模块.mp4 (38.3 MB)

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 (47.2 MB)

2-网络架构概述

2-网络架构概述.mp4 (32.4 MB)

3-流程与结果演示

3-流程与结果演示.mp4 (48.3 MB)

第六章：必备基础-物体检测FasterRcnn系列

1-三代算法-1-物体检测概述

三代算法-1-物体检测概述.mp4 (36.5 MB)

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 (39.1 MB)

3-三代算法-3-faster-rcnn概述

三代算法-3-faster-rcnn概述.mp4 (29.7 MB)

4-论文解读-1-论文整体概述

论文解读-1.mp4 (121.7 MB)

5-论文解读-2-RPN网络结构

论文解读-2-RPN网络结构.mp4 (114.1 MB)

6-论文解读-3-损失函数定义

论文解读-3-损失函数定义.mp4 (209.7 MB)

7-论文解读-4-网络细节

论文解读-4-网络细节.mp4 (266.8 MB)

04.Unet图像分割实战视频课程

1.mp4 (258.3 MB)

2.mp4 (199.7 MB)

3.mp4 (405.0 MB)

4.mp4 (199.0 MB)

5.mp4 (321.9 MB)

05.OpenCV图像处理课程资料

第2-7章notebook课件.zip (7.28 MB)

第八章notebook课件.zip (1.29 MB)

第九章：项目实战-信用卡数字识别.zip (548.1 KB)

第十章：项目实战-文档扫描OCR识别.zip (44.9 MB)

第11-12章notebook课件.zip (52.1 MB)

第十三章：案例实战-全景图像拼接.zip (829.5 KB)

第十四章：项目实战-停车场车位识别.zip (111.3 MB)

第十五章：项目实战-答题卡识别判卷.zip (3.07 MB)

第16-17章notebook课件.zip (9.37 MB)

第十八章：Opencv的DNN模块.zip (49.6 MB)

第十九章：项目实战-目标追踪.zip (125.3 MB)

第二十章：人脸关键点定位.zip (69.8 MB)

第二十一章：项目实战-疲劳检测.zip (74.1 MB)

06.YOLOV5目标检测课程资料

NEU-DET.zip (26.7 MB)

PyTorch-YOLOv3.zip (462.2 MB)

YOLO.pdf (1.88 MB)

07.MASK-RCNN课程资料

第六章：物体检测-faster-rcnn

Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks.pdf (6.49 MB)

faster-rcnn.pptx (3.23 MB)

FasterRcnn.zip (2.74 GB)

iccv15_tutorial_training_rbg.pdf (17.4 MB)

第二章：MaskRcnn网络框架源码详解.zip (1.14 GB)

第三章：基于MASK-RCNN框架训练自己的数据与任务.zip (439.4 MB)

第四章：练手小项目-人体姿态识别demo.zip (530.3 MB)

第五章：迁移学习.zip (91.9 MB)

08.Unet图像分割课程资料

深度学习分割任务.pdf (1.07 MB)

unet .zip (409.6 MB)

三：超详细人工智能学习大纲

人工智能大纲升级版本.pdf (20.3 MB)

四：机器学习基础算法教程

01.机器学习经典算法精讲视频课程

第一章：线性回归原理推导

0-课程简介.mp4 (34.9 MB)

1-回归问题概述.mp4 (19.7 MB)

2-误差项定义.mp4 (26.5 MB)

3-独立同分布的意义.mp4 (24.5 MB)

4-似然函数的作用.mp4 (29.0 MB)

5-参数求解.mp4 (30.7 MB)

6-梯度下降通俗解释.mp4 (20.8 MB)

7参数更新方法.mp4 (24.9 MB)

8-优化参数设置.mp4 (26.8 MB)

第二章：线性回归代码实现

第一章：线性回归

4-损失与预测模块

4-损失与预测模块.mp4 (46.7 MB)

5-数据与标签定义

5-数据与标签定义.mp4 (43.9 MB)

6-训练线性回归模型

6-训练线性回归模型.mp4 (44.7 MB)

8-整体流程debug解读

8-整体流程debug解读.mp4 (34.0 MB)

9-多特征回归模型

9-多特征回归模型.mp4 (62.2 MB)

10-非线性回归

10-非线性回归.mp4 (49.2 MB)

1-线性回归整体模块概述.mp4 (14.5 MB)

2-初始化步骤.mp4 (24.1 MB)

3-实现梯度下降优化模块.mp4 (39.6 MB)

7-得到线性回归方程.mp4 (35.8 MB)

第三章：模型评估方法

分类模型评估

1-Sklearn工具包简介

1-Sklearn工具包简介.mp4 (36.6 MB)

2-数据集切分

2-数据集切分.mp4 (25.8 MB)

3-交叉验证的作用

3-交叉验证的作用.mp4 (47.0 MB)

4-交叉验证实验分析

4-交叉验证实验分析.mp4 (64.5 MB)

5-混淆矩阵

5-混淆矩阵.mp4 (23.8 MB)

6-评估指标对比分析

6-评估指标对比分析.mp4 (52.2 MB)

7-阈值对结果的影响

7-阈值对结果的影响.mp4 (44.6 MB)

8-ROC曲线

8-ROC曲线.mp4 (31.4 MB)

第四章：线性回归实验分析

线性回归

2-参数直接求解方法

2-参数直接求解方法.mp4 (24.6 MB)

3-预处理对结果的影响

3-预处理对结果的影响.mp4 (54.8 MB)

4-梯度下降模块

4-梯度下降模块.mp4 (20.7 MB)

5-学习率对结果的影响

5-学习率对结果的影响.mp4 (32.3 MB)

6-随机梯度下降得到的效果

6-随机梯度下降得到的效果.mp4 (44.3 MB)

7-MiniBatch方法

7-MiniBatch方法.mp4 (31.2 MB)

8-不同策略效果对比

8-不同策略效果对比.mp4 (33.2 MB)

9-多项式回归

9-多项式回归.mp4 (37.6 MB)

10-模型复杂度

10-模型复杂度.mp4 (64.9 MB)

11-样本数量对结果的影响

11-样本数量对结果的影响.mp4 (60.4 MB)

12-正则化的作用

12-正则化的作用.mp4 (33.8 MB)

13-岭回归与lasso

13-岭回归与lasso.mp4 (91.4 MB)

14-实验总结

14-实验总结.mp4 (56.2 MB)

1-实验目标分析.mp4 (20.5 MB)

第五章：逻辑回归原理推导

1-逻辑回归算法原理.mp4 (23.0 MB)

2-化简与求解.mp4 (29.4 MB)

第六章：逻辑回归代码实现

第二章：逻辑回归

1-多分类逻辑回归整体思路

1-多分类逻辑回归整体思路.mp4 (20.6 MB)

2-训练模块功能

2-训练模块功能.mp4 (42.8 MB)

3-完成预测模块

3-完成预测模块.mp4 (36.7 MB)

4-优化目标定义

4-优化目标定义.mp4 (38.0 MB)

5-迭代优化参数

5-迭代优化参数.mp4 (49.9 MB)

6-梯度计算

6-梯度计算.mp4 (48.5 MB)

7-得出最终结果

7-得出最终结果.mp4 (55.3 MB)

8-鸢尾花数据集多分类任务

8-鸢尾花数据集多分类任务.mp4 (27.4 MB)

9-训练多分类模型

9-训练多分类模型.mp4 (47.7 MB)

10-准备测试数据

10-准备测试数据.mp4 (40.8 MB)

11-决策边界绘制

11-决策边界绘制.mp4 (55.8 MB)

12-非线性决策边界

12-非线性决策边界.mp4 (22.6 MB)

第七章：逻辑回归实验分析

1-逻辑回归实验概述.mp4 (52.2 MB)

2-概率结果随特征数值的变化.mp4 (46.7 MB)

3-可视化展示.mp4 (33.2 MB)

4-坐标棋盘制作.mp4 (38.2 MB)

5-分类决策边界展示分析.mp4 (61.1 MB)

6-多分类-softmax.mp4 (60.6 MB)

第八章：聚类算法-Kmeans&Dbscan原理

1-KMEANS算法概述.mp4 (28.9 MB)

2-KMEANS工作流程.mp4 (23.1 MB)

3-KMEANS迭代可视化展示.mp4 (31.7 MB)

4-DBSCAN聚类算法.mp4 (29.4 MB)

5-DBSCAN工作流程.mp4 (41.6 MB)

6-DBSCAN可视化展示.mp4 (33.0 MB)

第九章：Kmeans代码实现

第三章：聚类-Kmeans

1-Kmeans算法模块概述

Kmeans算法模块概述.mp4 (9.91 MB)

2-计算得到簇中心点

2-计算得到簇中心点.mp4 (24.1 MB)

3-样本点归属划分

3-样本点归属划分.mp4 (25.8 MB)

4-算法迭代更新

4-算法迭代更新.mp4 (27.9 MB)

5-鸢尾花数据集聚类任务

5-鸢尾花数据集聚类任务.mp4 (32.3 MB)

6-聚类效果展示

6-聚类效果展示.mp4 (52.4 MB)

第十章：聚类算法实验分析

聚类

1-Kmenas算法常用操作

1-Kmenas算法常用操作.mp4 (41.7 MB)

1-Kmenas算法常用操作_20190805_232034.mp4 (41.7 MB)

2-聚类结果展示

2-聚类结果展示.mp4 (19.6 MB)

2-聚类结果展示_20190805_232030.mp4 (19.6 MB)

3-建模流程解读

3-建模流程解读.mp4 (49.2 MB)

3-建模流程解读_20190805_232032.mp4 (49.2 MB)

4-不稳定结果

4-不稳定结果.mp4 (18.3 MB)

4-不稳定结果_20190805_232028.mp4 (18.3 MB)

5-评估指标-Inertia

5-评估指标-Inertia.mp4 (48.1 MB)

5-评估指标-Inertia_20190805_232027.mp4 (48.1 MB)

6-如何找到合适的K值

6-如何找到合适的K值.mp4 (34.7 MB)

6-如何找到合适的K值_20190805_232026.mp4 (34.7 MB)

7-轮廓系数的作用

7-轮廓系数的作用.mp4 (42.2 MB)

7-轮廓系数的作用_20190805_232028.mp4 (42.2 MB)

8-Kmenas算法存在的问题

8-Kmenas算法存在的问题.mp4 (34.3 MB)

8-Kmenas算法存在的问题_20190805_232023.mp4 (34.3 MB)

9-应用实例-图像分割

9-应用实例-图像分割.mp4 (39.4 MB)

9-应用实例-图像分割_20190805_232021.mp4 (39.4 MB)

10-半监督学习

10-半监督学习.mp4 (47.4 MB)

10-半监督学习_20190805_232033.mp4 (47.4 MB)

11-DBSCAN算法

11-DBSCAN算法.mp4 (55.5 MB)

11-DBSCAN算法_20190805_232033.mp4 (55.5 MB)

第十一章：决策树原理

1-决策树算法概述.mp4 (24.3 MB)

2-熵的作用.mp4 (22.8 MB)

3-信息增益原理.mp4 (30.3 MB)

4-决策树构造实例.mp4 (25.1 MB)

5-信息增益率与gini系数.mp4 (18.2 MB)

6-预剪枝方法.mp4 (25.1 MB)

7-后剪枝方法.mp4 (24.6 MB)

8-回归问题解决.mp4 (18.3 MB)

第十二章：决策树代码实现

第五章：决策树

1-整体模块概述

1-整体模块概述.mp4 (11.7 MB)

2-递归生成树节点

2-递归生成树节点.mp4 (27.7 MB)

3-整体框架逻辑

3-整体框架逻辑.mp4 (20.4 MB)

4-熵值计算

4-熵值计算.mp4 (40.1 MB)

5-数据集切分

5-数据集切分.mp4 (27.5 MB)

6-完成树模型构建

6-完成树模型构建.mp4 (27.9 MB)

7-测试算法效果

7-测试算法效果.mp4 (22.7 MB)

第十三章：决策树实验分析

决策树

1-树模型可视化展示

1-树模型可视化展示.mp4 (30.7 MB)

2-决策边界展示分析

2-决策边界展示分析.mp4 (41.2 MB)

3-树模型预剪枝参数作用

3-树模型预剪枝参数作用.mp4 (42.7 MB)

4-回归树模型

4-回归树模型.mp4 (41.7 MB)

课程简介

项目截图

1.png (160.1 KB)

QQ截图20190624141129.png (137.8 KB)

QQ截图20190624141231.png (103.5 KB)

QQ截图20190624141330.png (256.3 KB)

QQ截图20190624141428.png (140.9 KB)

Python机器学习实训营.docx (11.3 KB)

02.机器学习算法课件资料

部分代码资料

1-线性回归原理推导

2-回归算法.pdf (1.20 MB)

2-线性回归代码实现

线性回归-代码实现.zip (5.90 MB)

3-模型评估方法

img

1.png (150.1 KB)

2.png (110.5 KB)

3.png (81.8 KB)

4.png (110.4 KB)

5.png (73.1 KB)

6.png (114.9 KB)

7.png (114.3 KB)

8.png (74.8 KB)

9.png (121.6 KB)

模型评估方法.ipynb (91.2 KB)

3-线性回归实验分析

线性回归-实验.zip (643.3 KB)

5-逻辑回归代码实现

逻辑回归-代码实现.zip (5.04 MB)

6-逻辑回归实验分析

逻辑回归-实验.zip (1.70 MB)

7-聚类算法-Kmeans&Dbscan原理

4-聚类算法.pdf (788.3 KB)

8-Kmeans代码实现

Kmeans-代码实现.zip (5.03 MB)

9-聚类算法实验分析

mldata

mnist-original.mat (52.9 MB)

聚类算法-实验.zip (1.71 MB)

10-决策树原理

3-决策树与集成算法.pdf (1.00 MB)

11-决策树代码实现

决策树-代码实现.zip (6.14 KB)

12-决策树实验分析

决策树算法-实验.zip (284.6 KB)

13-集成算法原理

3-决策树与集成算法.pdf (1.00 MB)

14-集成算法实验分析

mldata

mnist-original.mat (52.9 MB)

随机森林与集成算法-实验.zip (11.9 MB)

15-支持向量机原理推导

6-支持向量机.pdf (1.29 MB)

机器学习算法PPT

1-AI入学指南.pdf (658.6 KB)

2-回归算法.pdf (1.20 MB)

3-决策树与集成算法.pdf (1.00 MB)

4-聚类算法.pdf (788.3 KB)

5-贝叶斯算法.pdf (539.5 KB)

6-支持向量机.pdf (1.29 MB)

7-推荐系统.pdf (1.97 MB)

8-xgboost.pdf (932.1 KB)

9-LDA与PCA算法.pdf (1.04 MB)

10-EM算法.pdf (811.4 KB)

11-神经网络.pdf (11.7 MB)

12-word2vec.pdf (2.37 MB)

时间序列分析.pdf (767.3 KB)

文本分析.pdf (522.2 KB)

五：深度学习神经网络基础教程

CNN卷积神经网络基础

1-卷积运算详解-1.mp4 (30.8 MB)

2-卷积运算详解-2.mp4 (28.0 MB)

3-卷积运算详解-3.mp4 (28.4 MB)

4-卷积运算详解-4.mp4 (21.9 MB)

5-卷积神经网络图解-1.mp4 (36.9 MB)

6-卷积神经网络图解-2.mp4 (30.4 MB)

7-卷积神经网络图解-3.mp4 (25.8 MB)

8-卷积神经网络图解-4.mp4 (26.5 MB)

9-池化与采样操作讲解.mp4 (17.0 MB)

10-CIFAR100与VGG13实战-1.mp4 (14.0 MB)

11-CIFAR100与VGG13实战-2.mp4 (13.7 MB)

12-CIFAR100与VGG13实战-3.mp4 (14.8 MB)

13-CIFAR100与VGG13实战-4.mp4 (10.4 MB)

14-经典卷积神经网络详解-1.mp4 (16.7 MB)

15-经典卷积神经网络详解-2.mp4 (13.2 MB)

17-BatchNorm-2.mp4 (30.3 MB)

18-ResNet, DenseNet详解.mp4 (18.1 MB)

19-ResNet, DenseNet详解.mp4 (19.0 MB)

20-ResNet实战-1.mp4 (14.3 MB)

21-ResNet实战-2.mp4 (14.5 MB)

22-ResNet实战-3.mp4 (15.1 MB)

23-ResNet实战-4.mp4 (17.5 MB)

GAN对抗生成网络基础

1 数据的分布.flv (17.7 MB)

2 画家的成长历程.flv (29.5 MB)

3 生成对抗网络.flv (25.8 MB)

4 纳什均衡-1.flv (19.2 MB)

5 纳什均衡-2.flv (35.4 MB)

6 GAN训练难题.flv (37.0 MB)

7 EM距离.flv (19.2 MB)

8 WGAN-GP原理.flv (30.6 MB)

9 GAN实战-1.flv (16.8 MB)

10 GAN实战-2.flv (33.5 MB)

11 WGAN实战-1.flv (19.7 MB)

12 WGAN实战-2.flv (35.2 MB)

RNN循环神经网络基础

1. 课时1 时间序列介绍.mp4 (21.6 MB)

2. 课时2 循环神经网络基本原理-1.mp4 (13.7 MB)

3. 课时3 循环神经网络基本原理-2.mp4 (16.3 MB)

4. 课时4 循环神经网络中Layer使用-1.mp4 (16.0 MB)

5. 课时5 循环神经网络中Layer的使用-2.mp4 (14.4 MB)

6. 课时6 项目实战-时间序列预测问题.mp4 (19.6 MB)

7. 课时7 LSTM基本原理-1.mp4 (13.0 MB)

8. 课时8 LSTM基本原理-2.mp4 (16.2 MB)

9. 课时9 LSTM中Layer的使用.mp4 (12.2 MB)

10. 课时10 RNN训练难题—梯度弥散与梯度爆炸.mp4 (23.9 MB)

11. 课时11 项目实战-情感分类问题.mp4 (33.5 MB)

神经网络模型基础课件资料

CNN RNN GAN

课程安装软件-Ubuntu 18.04

Anaconda3-2019.03-Linux-x86_64.sh (654.1 MB)

cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb (1.55 GB)

cudnn-10.0-linux-x64-v7.5.0.56.tgz (412.8 MB)

课程安装软件-Win10

Anaconda3-2019.03-Windows-x86_64.exe (661.7 MB)

cuda_10.0.130_411.31_win10.exe (2.04 GB)

cudnn-10.0-windows10-x64-v7.5.0.56 (1).zip (213.8 MB)

pycharm-community-2019.1.1.exe (231.8 MB)

源代码和PPT在Github下载.txt (72.0 B)

Deep-Learning-with-PyTorch-Tutorials.zip (80.9 MB)

一：人工智能论文合集

CNN_不能错过的10篇论文

1311.2524v5_R_CNN.pdf (6.23 MB)

1311.2901v3_Visualizing and Understanding Convolutional Networks.pdf (34.6 MB)

1406.2661v1_Generative Adversarial Nets.pdf (518.0 KB)

1409.1556v6_VERY DEEP CONVOLUTIONAL Networks.pdf (195.3 KB)

1412.2306v2_Deep Visual-Semantic Alignments for Generating Image Descriptions.pdf (5.21 MB)

1504.08083_Fast R-CNN.pdf (714.0 KB)

1506.01497v3_Faster R-CNN.pdf (6.59 MB)

1506.02025_Spatial Transformer Networks.pdf (7.89 MB)

1512.03385v1_Deep Residual Learning for Image Recognition.pdf (800.2 KB)

4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf (1.35 MB)

Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf (1.24 MB)

cvpr2021

解压密码：cvpr2021

CVPR行人重识别论文解读

1. 1-关键点位置特征构建.mp4 (18.0 MB)

2. 2-图卷积与匹配的作用.mp4 (20.8 MB)

4. 3-局部特征热度图计算.mp4 (21.1 MB)

5. 4-基于图卷积构建人体拓扑关系.mp4 (25.9 MB)

6. 5-图卷积模块实现方法.mp4 (23.5 MB)

ICCV2021

解压密码： iccv2021

Resnet论文解读

13-额外补充-Resnet论文解读.mp4 (117.4 MB)

深度学习论文精讲-BERT模型

1. 课程介绍.mp4 (36.9 MB)

2. 1-论文讲解思路概述.mp4 (14.8 MB)

3. 2-BERT模型摘要概述.mp4 (32.3 MB)

4. 3-模型在NLP领域应用效果.mp4 (33.6 MB)

5. 4-预训练模型的作用.mp4 (18.4 MB)

6. 5-输入数据特殊编码字符解析.mp4 (43.9 MB)

7. 6-向量特征编码方法.mp4 (24.6 MB)

8. 7-BERT模型训练策略.mp4 (43.0 MB)

9. 8-论文总结分析.mp4 (76.4 MB)

图神经网络（GNN）100篇论文集

Applications

combinatorial optimization

Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search(1).pdf (537.0 KB)

Learning Combinatorial Optimization Algorithms over Graphs.pdf (2.91 MB)

graph generation

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.pdf (518.0 KB)

MolGAN- An implicit generative model for small molecular graphs(1).pdf (1.10 MB)

NetGAN- Generating Graphs via Random Walks(1).pdf (1.67 MB)

image

Image classification

Few-Shot Learning with Graph Neural Networks.pdf (1.69 MB)

Interaction Detection

Structural-RNN- Deep Learning on Spatio-Temporal Graphs.pdf (1.10 MB)

Object Detection

Learning Region features for Object Detection.pdf (1.68 MB)

Relation Networks for Object Detection.pdf (906.7 KB)

Region Classification

Iterative Visual Reasoning Beyond Convolutions..pdf (3.91 MB)

Semantic Segmentation

3D Graph Neural Networks for RGBD Semantic Segmentation.pdf (2.23 MB)

Dynamic Graph CNN for Learning on Point Clouds.pdf (5.07 MB)

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs.pdf (4.83 MB)

Modeling polypharmacy side effects with graph convolutional networks.pdf (4.18 MB)

PointNet- Deep Learning on Point Sets for 3D Classification and Segmentation.pdf (8.66 MB)

Visual Question Answering

Graph-Structured Representations for Visual Question Answering.pdf (3.74 MB)

Out of the Box- Reasoning with Graph Convolution Nets for Factual Visual Question Answering(1).pdf (2.45 MB)

knowledge graph

Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks.pdf (432.6 KB)

Deep Reasoning with Knowledge Graph for Social Relationship Understanding.pdf (2.76 MB)

Dynamic Graph Generation Network- Generating Relational Knowledge from Diagrams.pdf (1.19 MB)

Knowledge Transfer for Out-of-Knowledge-Base Entities - A Graph Neural Network Approach.pdf (355.2 KB)

Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering.pdf (437.8 KB)

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs.pdf (1.36 MB)

Representation learning for visual-relational knowledge graphs.pdf (6.90 MB)

The More You Know- Using Knowledge Graphs for Image Classification.pdf (2.31 MB)

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs.pdf (1.63 MB)

science

A Compositional Object-Based Approach to Learning Physical Dynamics.pdf (4.26 MB)

A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.pdf (340.4 KB)

A simple neural network module for relational reasoning.pdf (1.37 MB)

Action Schema Networks- Generalised Policies with Deep Learning.pdf (1.67 MB)

Adversarial Attack on Graph Structured Data.pdf (593.1 KB)

Attend, Infer, Repeat- Fast Scene Understanding with Generative Models.pdf (1.30 MB)

Attention, Learn to Solve Routing Problems!.pdf (1.48 MB)

Beyond Categories- The Visual Memex Model for Reasoning About Object Relationships.pdf (618.7 KB)

Combining Neural Networks with Personalized PageRank for Classification on Graphs.pdf (483.2 KB)

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders.pdf (567.1 KB)

Constructing Narrative Event Evolutionary Graph for Script Event Prediction.pdf (654.9 KB)

Conversation Modeling on Reddit using a Graph-Structured LSTM.pdf (682.3 KB)

Convolutional networks on graphs for learning molecular fingerprints.pdf (785.4 KB)

Cross-Sentence N-ary Relation Extraction with Graph LSTMs.pdf (540.9 KB)

Deep Graph Infomax.pdf (8.15 MB)

DeepInf- Modeling influence locality in large social networks.pdf (1.07 MB)

Discovering objects and their relations from entangled scene representations.pdf (4.99 MB)

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.pdf (567.1 KB)

Effective Approaches to Attention-based Neural Machine Translation.pdf (244.0 KB)

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks.pdf (6.99 MB)

Graph Convolutional Matrix Completion.pdf (733.0 KB)

Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf (9.84 MB)

Graph networks as learnable physics engines for inference and control.pdf (2.72 MB)

GraphRNN- Generating Realistic Graphs with Deep Auto-regressive Models.pdf (2.43 MB)

Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification.pdf (2.52 MB)

Hyperbolic Attention Networks.pdf (3.08 MB)

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.pdf (304.2 KB)

Inference in Probabilistic Graphical Models by Graph Neural Networks.pdf (3.07 MB)

Interaction Networks for Learning about Objects, Relations and Physics.pdf (1.91 MB)

Learning a SAT Solver from Single-Bit Supervision.pdf (1.89 MB)

Learning Conditioned Graph Structures for Interpretable Visual Question Answering.pdf (8.48 MB)

Learning Deep Generative Models of Graphs.pdf (2.31 MB)

Learning Graphical State Transitions.pdf (1.47 MB)

Learning Human-Object Interactions by Graph Parsing Neural Networks.pdf (3.91 MB)

Learning model-based planning from scratch.pdf (1.28 MB)

Learning Multiagent Communication with Backpropagation.pdf (4.13 MB)

Learning to Represent Programs with Graphs.pdf (421.9 KB)

Metacontrol for Adaptive Imagination-Based Optimization.pdf (1.60 MB)

Molecular Graph Convolutions- Moving Beyond Fingerprints.pdf (2.08 MB)

NerveNet Learning Structured Policy with Graph Neural Networks.pdf (3.11 MB)

Neural Combinatorial Optimization with Reinforcement Learning.pdf (393.2 KB)

Neural Module Networks.pdf (1.03 MB)

Neural Relational Inference for Interacting Systems.pdf (2.83 MB)

Protein Interface Prediction using Graph Convolutional Networks.pdf (837.8 KB)

Relational Deep Reinforcement Learning.pdf (6.81 MB)

Relational inductive bias for physical construction in humans and machines.pdf (1,022.5 KB)

Relational neural expectation maximization- Unsupervised discovery of objects and their interactions.pdf (1.15 MB)

Self-Attention with Relative Position Representations.pdf (229.9 KB)

Semi-supervised User Geolocation via Graph Convolutional Networks.pdf (1.13 MB)

Situation Recognition with Graph Neural Networks.pdf (5.27 MB)

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.pdf (1.50 MB)

Spatio-Temporal Graph Convolutional Networks- A Deep Learning Framework for Traffic Forecasting.pdf (895.0 KB)

Structured Dialogue Policy with Graph Neural Networks.pdf (779.2 KB)

Symbolic Graph Reasoning Meets Convolutions.pdf (3.23 MB)

Traffic Graph Convolutional Recurrent Neural Network- A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.pdf (1.45 MB)

Translating Embeddings for Modeling Multi-relational Data.pdf (414.2 KB)

Understanding Kin Relationships in a Photo.pdf (1.44 MB)

VAIN- Attentional Multi-agent Predictive Modeling.pdf (424.0 KB)

Visual Interaction Networks- Learning a Physics Simulator from Vide.o.pdf (5.41 MB)

text

A Graph-to-Sequence Model for AMR-to-Text Generation.pdf (290.2 KB)

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling.pdf (621.9 KB)

End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures.pdf (363.1 KB)

Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks.pdf (604.6 KB)

Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks..pdf (453.5 KB)

Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.pdf (784.4 KB)

Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.pdf (346.9 KB)

Graph Convolutional Networks for Text Classification.pdf (1.83 MB)

Graph Convolutional Networks with Argument-Aware Pooling for Event Detection.pdf (324.7 KB)

Jointly Multiple Events Extraction via Attention-based Graph.pdf (430.4 KB)

N-ary relation extraction using graph state LSTM.pdf (455.7 KB)

Recurrent Relational Networks.pdf (307.0 KB)

Models

graph_type

directed graph

Rethinking Knowledge Graph Propagation for Zero-Shot Learning.pdf (4.21 MB)

edge-informative graph

Graph-to-Sequence Learning using Gated Graph Neural Networks.pdf (4.06 MB)

Modeling relational data with graph convolutional networks.pdf (323.6 KB)

Adaptive Graph Convolutional Neural Networks.pdf (803.9 KB)

Graph Capsule Convolutional Neural Networks.pdf (1.93 MB)

Graph Neural Networks for Object Localization.pdf (221.8 KB)

Graph Neural Networks for Ranking Web Pages.pdf (1.01 MB)

Graph Partition Neural Networks for Semi-Supervised Classification.pdf (713.9 KB)

How Powerful are Graph Neural Networks-.pdf (678.3 KB)

Mean-field theory of graph neural networks in graph partitioning.pdf (369.4 KB)

others

A Comparison between Recursive Neural Networks and Graph Neural Networks.pdf (247.2 KB)

A new model for learning in graph domains.pdf (177.6 KB)

CelebrityNet- A Social Network Constructed from Large-Scale Online Celebrity Images.pdf (16.3 MB)

Contextual Graph Markov Model- A Deep and Generative Approach to Graph Processing.pdf (570.6 KB)

Deep Sets.pdf (5.11 MB)

Deriving Neural Architectures from Sequence and Graph Kernels.pdf (687.0 KB)

Diffusion-Convolutional Neural Networks.pdf (366.4 KB)

Geometric deep learning on graphs and manifolds using mixture model cnns.pdf (7.23 MB)

propagation_type

attention

Attention Is All You Need.pdf (2.10 MB)

Graph Attention Networks.pdf (1.48 MB)

Graph Classification using Structural Attention.pdf (2.47 MB)

convolution

Bayesian Semi-supervised Learning with Graph Gaussian Processes.pdf (689.9 KB)

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.pdf (459.4 KB)

Deep Convolutional Networks on Graph-Structured Data.pdf (4.57 MB)

Learning Convolutional Neural Networks for Graphs.pdf (639.9 KB)

Spectral Networks and Deep Locally Connected.pdf (1.86 MB)

Structure-Aware Convolutional Neural Networks.pdf (1.36 MB)

gate

Gated Graph Sequence Neural Networks.pdf (748.2 KB)

Sentence-State LSTM for Text Representation.pdf (442.3 KB)

skip

Representation Learning on Graphs with Jumping Knowledge Networks.pdf (3.15 MB)

Semi-Supervised Classification with Graph Convolutional Networks.pdf (853.4 KB)

training methods

boosting

Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning.pdf (1.96 MB)

neighborhood sampling

Adaptive Sampling Towards Fast Graph Representation Learning.pdf (580.0 KB)

FastGCN- Fast Learning with Graph Convolutional Networks via Importance Sampling.pdf (358.3 KB)

Inductive Representation Learning on Large Graphs.pdf (1.04 MB)

receptive field control

Stochastic Training of Graph Convolutional Networks with Variance Reduction.pdf (1.25 MB)

Covariant Compositional Networks For Learning Graphs.pdf (482.5 KB)

Graphical-Based Learning Environments for Pattern Recognition.pdf (335.9 KB)

Hierarchical Graph Representation Learning with Differentiable Pooling.pdf (2.31 MB)

Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction.pdf (1,000.5 KB)

Learning Steady-States of Iterative Algorithms over Graphs.pdf (3.09 MB)

Neural networks for relational learning- an experimental comparison.pdf (1.15 MB)

Survey

极力推荐

Graph Neural Networks：A Review of Methods and Applications.pdf (2.67 MB)

Non-local Neural Networks.pdf (1.24 MB)

Relational Inductive Biases, Deep Learning, and Graph Networks.pdf (8.99 MB)

The Graph Neural Network Model.pdf (1.43 MB)

一般推荐

A Comprehensive Survey on Graph Neural Networks.pdf (1.80 MB)

Computational Capabilities of Graph Neural Networks(1).pdf (1.28 MB)

Deep Learning on Graphs- A Survey.pdf (1.80 MB)

Geometric Deep Learning- Going beyond Euclidean data.pdf (5.26 MB)

Neural Message Passing for Quantum Chemistry.pdf (511.1 KB)

论文集索引.jpg (29.7 KB)

下载链接

点我免费下载

资源预览