"Machine Learning"의 두 판 사이의 차이
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| 1번째 줄: | 1번째 줄: | ||
| − | + | * #ril | |
| − | * | ||
** https://medium.com/technologymadeeasy/the-best-explanation-of-convolutional-neural-networks-on-the-internet-fbb8b1ad5df8 | ** https://medium.com/technologymadeeasy/the-best-explanation-of-convolutional-neural-networks-on-the-internet-fbb8b1ad5df8 | ||
** http://nmhkahn.github.io/Casestudy-CNN | ** http://nmhkahn.github.io/Casestudy-CNN | ||
| 6번째 줄: | 5번째 줄: | ||
** https://www.quora.com/What-exactly-is-the-degradation-problem-that-Deep-Residual-Networks-try-to-alleviate | ** https://www.quora.com/What-exactly-is-the-degradation-problem-that-Deep-Residual-Networks-try-to-alleviate | ||
* [[CUDA기타설치]] | * [[CUDA기타설치]] | ||
| − | * | + | * nets |
** [[dilated cnn]] | ** [[dilated cnn]] | ||
** [[pathnet]] | ** [[pathnet]] | ||
| 18번째 줄: | 17번째 줄: | ||
* [[Recommendation]] | * [[Recommendation]] | ||
* [[YOLO]] | * [[YOLO]] | ||
| − | |||
2017년 6월 15일 (목) 11:20 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
- nets
- dilated cnn
- pathnet
- ResNet
- GoogLeNet (Inception)
- Learning to learn by GD by GD
- Face
- Person re-identification
- Generative Models
- Batch Normalization
- Recommendation
- YOLO