4.caffe资源汇总（更新中）

学习需要更新，网上有一些非常不错博客.

感谢这些博主，他们都很认真。

00、tornadomeet

0、denny的学习专栏

1、xizero00

2、lingerlanlan

3、iamzhangzhuping

4、zhangwang

5、yhl_leo

6、在路上

7、seven_first

８、Omer Shamir

9、ycheng_sjtu

10、samylee

11、神经网络入门

12、Caffe快速入门

13、CNN的反向传播

14、caffe源码学习笔记

15、CNN入门基础：感知域说的很清楚

16、CNN入门

17、caffe使用基础(星空下的巫师)c++版本

18、caffe+CNN

19、visual conNet CNN的可视化

20、CNN softmax公式推导

21、CNN人脸检测 (matConvet)

22、CS231 CNN 课程

23、Visualizing and understandingConvolutionalNetworks视频

24、返卷积的概念

https://github.com/vdumoulin/conv_arithmetic

http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf

25、CNN的反向传播，讲的很好

26、google深度学习笔记视频

https://classroom.udacity.com/courses/ud730/lessons/6370362152/concepts/63798118170923

 27、caffe源码全连接层分析

28、caffe训练CNN流程

29、CNN中的一些trick

30、通过BN来理解bp传播

http://blog.csdn.net/hjimce/article/details/50866313

31、CNN batch normalization Caffe和mxNet

32、building-blocks-of-deep-learning

33、CS231实现自己的卷积和BN

http://cthorey.github.io./backprop_conv/

http://cthorey.github.io./backpropagation/

34、caffe源码系列

http://blog.csdn.net/xizero00/article/category/5619855/1

http://blog.csdn.net/langb2014/article/details/51543388

35、CVPR 2015的讨论

36、Memect

37、liumaolincycle

38、thy_2014

39、牛闯

40、liyaohhh

神经网络入门:

http://neuralnetworksanddeeplearning.com/chap1.html

 

Caffe快速入门

http://shengshuyang.github.io/A-step-by-step-guide-to-Caffe.html

 

CNN的反向传播

http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/

 

caffe源码学习笔记

 

http://46aae4d1e2371e4aa769798941cef698.devproxy.yunshipei.com/seven_first/article/category/5721883/

 

CNN入门基础：感知域说的很清楚

https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/

 

CNN入门

http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/

 

caffe使用基础(星空下的巫师)c++版本

https://github.com/shicai/Caffe_Manual

 

caffe+CNN

http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/

 

visual conNet CNN的可视化

 

https://github.com/jcjohnson/cnn-vis/

 

CNN softmax公式推导

http://zjjconan.github.io/articles/2015/04/Softmax-Regression-Matlab/

 

CNN人脸检测 (matConvet)

https://github.com/willard-yuan/CNN-for-Face-Image-Retrieval

 

 

CS231 CNN 课程

http://cs231n.github.io/neural-networks-3/

 

Visualizing and understandingConvolutionalNetworks视频

http://videolectures.net/eccv2014_zeiler_convolutional_networks/

 

返卷积的概念：

http://datascience.stackexchange.com/questions/6107/what-are-deconvolutional-layers

 

https://github.com/vdumoulin/conv_arithmetic

http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf

 

 

CNN的反向传播，讲的很好

http://www.cnblogs.com/tornadomeet/p/3468450.html

 

google深度学习笔记视频

http://www.jianshu.com/p/c2a870c19623

https://classroom.udacity.com/courses/ud730/lessons/6370362152/concepts/63798118170923

 

caffe源码全连接层分析

http://zhangliliang.com/2014/09/15/about-caffe-code-full-connected-layer/

 

caffe训练CNN流程

https://frankzliu.com/experimenting-with-different-penultimate-layers-in-caffe/

 

CNN中的一些trick

http://lamda.nju.edu.cn/weixs/project/CNNTricks/CNNTricks.html

 

通过BN来理解bp 传播

https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html

http://blog.csdn.net/hjimce/article/details/50866313

 

CNN batch normalization Caffe和mxNet

http://shuokay.com/2016/05/28/batch-norm/

http://www.it610.com/article/5204719.htm

 

building-blocks-of-deep-learning

 

http://deepdish.io/2015/11/21/building-blocks-of-deep-learning/

 

CS231实现自己的卷积和BN

http://cthorey.github.io./backprop_conv/

http://cthorey.github.io./backpropagation/

 

caffe源码系列

http://blog.csdn.net/xizero00/article/category/5619855/1

http://blog.csdn.net/langb2014/article/details/51543388

 

CVPR 2015的讨论

http://www.computervisionblog.com/2015/06/deep-down-rabbit-hole-cvpr-2015-and.html