博主github:https://github.com/MichaelBeechan
博主CSDN:https://blog.csdn.net/u011344545
下载链接:https://download.csdn.net/download/u011344545/11170869
deeplearning.mit.edu
MIT:https://github.com/lexfridman/mit-deep-learning
MIT:https://github.com/lexfridman
1、自然语言处理
More details: http://jalammar.github.io/illustrated-transformer/
Vaswani, Ashish, et al. “Attention is all you need.” Advances in Neural Information Processing Systems. 2017.
Devlin, Jacob, et al. “Bert: Pre-training of deep bidirectional transformers for language understanding.” (2018).
翻译:https://blog.csdn.net/muumian123/article/details/84031163
2、特斯拉自动驾驶仪硬件v2+:大规模神经网络
3、AdaNet:自动集成电路
4、AutoAugment: 深度RL数据增强
5、用合成数据训练深度网络
Tremblay, Jonathan, et al. “Training deep networks with synthetic data: Bridging the reality gap by domain randomization.” (2018).
6、多边形RNN ++分割注释
Acuna, David, et al. “Efficient Interactive Annotation of Segmentation Datasets With Polygon-RNN++.” CVPR 2018.
7、DAWNBench: Training Fast and Cheap
Details: http://bit.ly/2H6yv6H
8、BigGAN:图像合成的最新技术
Brock, Andrew, Jeff Donahue, and Karen Simonyan. “Large scale gan training for high fidelity natural image synthesis.” (2018).
9、Video-to-Video Synthesis
Wang, Ting-Chun, et al. “Video-to-video synthesis.” (2018)
10、语义分割
Tutorial: https://github.com/lexfridman/mit-deep-learning
11、AlphaZero & OpenAI Five
12、深度学习框架
• Videos and slides posted on the website
• Code posted on GitHub: https://github.com/lexfridman/mit-deep-learning