"Machine Learning"의 두 판 사이의 차이
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| 5번째 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]] | ||
| − | * [[dilated cnn]] | + | * nets |
| + | ** [[dilated cnn]] | ||
| + | ** [[pathnet]] | ||
| + | ** [[ResNet]] | ||
| + | ** [[GoogLeNet]] (Inception) | ||
* [[Learning to learn by GD by GD]] | * [[Learning to learn by GD by GD]] | ||
* [[Face]] | * [[Face]] | ||
* [[Person re-identification]] | * [[Person re-identification]] | ||
* [[Generative Models]] | * [[Generative Models]] | ||
| − | |||
* [[Batch Normalization]] | * [[Batch Normalization]] | ||
| − | |||
| − | |||
* [[Recommendation]] | * [[Recommendation]] | ||
* [[YOLO]] | * [[YOLO]] | ||
2017년 6월 14일 (수) 16:19 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