"Machine Learning"의 두 판 사이의 차이
ph
| 3번째 줄: | 3번째 줄: | ||
** http://nmhkahn.github.io/Casestudy-CNN | ** http://nmhkahn.github.io/Casestudy-CNN | ||
** https://stats.stackexchange.com/questions/205150/how-do-bottleneck-architectures-work-in-neural-networks | ** https://stats.stackexchange.com/questions/205150/how-do-bottleneck-architectures-work-in-neural-networks | ||
| + | ** https://www.quora.com/What-exactly-is-the-degradation-problem-that-Deep-Residual-Networks-try-to-alleviate | ||
* [[CUDA기타설치]] | * [[CUDA기타설치]] | ||
* [[dilated cnn]] | * [[dilated cnn]] | ||
2017년 5월 24일 (수) 18:47 판
- #ril
- https://medium.com/technologymadeeasy/the-best-explanation-of-convolutional-neural-networks-on-the-internet-fbb8b1ad5df8
- http://nmhkahn.github.io/Casestudy-CNN
- https://stats.stackexchange.com/questions/205150/how-do-bottleneck-architectures-work-in-neural-networks
- https://www.quora.com/What-exactly-is-the-degradation-problem-that-Deep-Residual-Networks-try-to-alleviate
- CUDA기타설치
- dilated cnn
- Learning to learn by GD by GD
- Face
- Person re-identification
- Generative Models
- pathnet
- Batch Normalization
- GoogLeNet (Inception)