Deep Learning各种资料网址

深度学习应用干货奉上:

1、自然语言处理(1):Word Embedding--介绍

zouxy9的博客:Deep Learning(深度学习):学习笔记整理,一共八篇,是很基础的内容

http://blog.csdn.net/zouxy09/article/details/8775360/

有趣的机器学习:最简明入门指南

http://blog.jobbole.com/67616/

深度学习如何入门?

http://www.zhihu.com/question/26006703

Residual Networks :

介绍一下2015 ImageNet中分类任务的冠军——MSRA何凯明团队的Residual Networks

http://blog.csdn.net/abcjennifer/article/details/50514124

image classification with deep learning常用模型

image classification常用的cnn模型,针对cifar10(for 物体识别),mnist(for 字符识别)& ImageNet(for 物体识别)做一个model 总结 介绍了一下这些网络的结构

http://blog.csdn.net/abcjennifer/article/details/42493493

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection 论文讲解,Faster-rcnn中的proposal提取网络RPN由于特征图的粗糙,在小目标及大IOU阈值情况下的检测率低。论文提出了HyperNet,综合低层,中间层和高层特征获得了较高的recall率http://blog.csdn.net/cv_family_z/article/details/51135025

目标检测“A MultiPath Network for Object Detection”

对Fast-RCNN方法做了三个小的修改:(1)检测器能够访问多层特征,(2)foveal结构多尺度提取目标上下文信息,(3)在多个IOU下优化损失函数

http://blog.csdn.net/cv_family_z/article/details/51159619

跟踪“Visual Tracking with Fully Convolutional Networks”

对VGG16特征分析

http://blog.csdn.net/cv_family_z/article/details/50748236

Going deeper with convolutions

Googlenet,22层的深度网络。充分利用了网络中的计算资源,通过增加网络的宽度及深度实现。

http://blog.csdn.net/cv_family_z/article/details/50603406

SSD: Single Shot MultiBox Detector

本文算是 Faster R-CNN, YOLO 算法的改进版吧,它将检测和分类融合到一起去了,对每个可能的检测框赋予一个类别的概率。

http://blog.csdn.net/cv_family_z/article/details/50474679

Striving for Simplicity: The All Convolutional Net :全卷积网络

http://blog.csdn.net/cv_family_z/article/details/50403365

From Facial Parts Responses to Face Detection: A Deep Learning Approach:公开代码,用CNN进行人脸局部属性检测,然后各个部件综合起来得到人脸检测结果。

http://blog.csdn.net/cv_family_z/article/details/50233481

论文提要 Deep Face Recognition:公开代码

http://blog.csdn.net/cv_family_z/article/details/49868979

DeepID-Net:multi-stage and deformable deep CNNs for object detection:Rcnn改进

http://blog.csdn.net/cv_family_z/article/details/49588969

行人检测“Pedestrian Detection with Unsupervised Multi-Stage Feature Learning”

http://blog.csdn.net/cv_family_z/article/details/49276833

车型识别“Vehicle Type Classification Using a Semisupervised Convolutional Neural Network"

http://blog.csdn.net/cv_family_z/article/details/49154585

论文提要“Learning Deepface Representation”

http://blog.csdn.net/cv_family_z/article/details/48975027

论文提要“Taking a Deeper Look at Pedestrians”

http://blog.csdn.net/cv_family_z/article/details/48053535

论文提要“Pedestrian Detection aided by Deep Learning Semantic Tasks”

http://blog.csdn.net/cv_family_z/article/details/47259677

如何简单形象又有趣地讲解神经网络是什么?

http://daily.zhihu.com/story/4424412

深度学习笔记1(卷积神经网络)

http://blog.csdn.net/lu597203933/article/details/46575779

DeepLearnToolBox中CNN源码解析

http://blog.csdn.net/lu597203933/article/details/46576017

CNN(卷积神经网络)、RNN(循环神经网络)、DNN(深度神经网络)的内部网络结构有什么区别

https://www.zhihu.com/question/34681168

针对Faster RCNN具体细节以及源码的解读之SmoothL1Loss层

http://blog.csdn.net/xyy19920105/article/details/50421225

归一化化定义

http://www.cnblogs.com/njustyxy/archive/2011/06/10/2077926.html

UFLDL中文教程

http://ufldl.stanford.edu/wiki/index.php/UFLDL教程

介绍:使用卷积神经网络的图像缩放.

http://engineering.flipboard.com/2015/05/scaling-convnets/

归一化化定义

http://www.cnblogs.com/njustyxy/archive/2011/06/10/2077926.html

基于Theano的深度学习(Deep Learning)框架Keras学习随笔-12-核心层

如何在Caffe中配置每一个层的结构

http://demo.netfoucs.com/danieljianfeng/article/details/42929283

本文来源:

人工智能大数据与深度学习

  • 发表于:
  • 原文链接http://kuaibao.qq.com/s/20171229A0WJZR00?refer=cp_1026
  • 腾讯「云+社区」是腾讯内容开放平台帐号(企鹅号)传播渠道之一,根据《腾讯内容开放平台服务协议》转载发布内容。

扫码关注云+社区

领取腾讯云代金券