Awesome-cad : A Repository for Paper Reading and Code Sharing

awesome-cad支持以下工作:

• 新潮文章解读，附上链接，博客，代码
• 技巧性强的或有趣工作的代码
• 组会讨论的代码
• 好用工具

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如何将近期Paper Reading和可用代码及其解读添加进仓库

1. fork此项目至用户账号下
2. git clone用户账号的该项目
3. 修改README.md；对于长篇文章解读，新建文件,修改完
4. git add .
5. git commit -m "add new paper"
6. git push origin master
7. 到图形界面下，点击New pull request提交修改

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注意事项:

1. 代码尽量先在个人账号下存储，对于awesome-cad只提供链接和说明，以及运行注意事项等，保证项目整洁不混乱。
2. 所加内容尽量自己仔细看过
3. 添加内容采用以下规范：

• Microsoft (Deep Residual Learning) [Paper][Slide]
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385.
• Microsoft (PReLu/Weight Initialization) [Paper]
  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, arXiv:1502.01852.
• Batch Normalization [Paper]
  • Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167.
• GoogLeNet [Paper]
  • Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR, 2015.
• VGG-Net [Web] [Paper]
  • Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Visual Recognition, ICLR, 2015.
• AlexNet [Paper]
  • Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012.