Deep Learning（深度学习）各种网址（持续更新）

 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 <2015 ICCV, ImageNet 图像分类Top1>   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

http://blog.csdn.net/niuwei22007/article/details/49277595

如何在Caffe中配置每一个层的结构

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