"Machine Learning"의 두 판 사이의 차이
ph
잔글 (→general) |
잔글 (→general) |
||
| 21번째 줄: | 21번째 줄: | ||
** [[dilated cnn]] | ** [[dilated cnn]] | ||
** [[pathnet]] | ** [[pathnet]] | ||
| + | ** [[ResNet]] | ||
** [[Fast RCNN]] | ** [[Fast RCNN]] | ||
** [[Faster RCNN]] | ** [[Faster RCNN]] | ||
| − | ** [[ | + | ** [[R-FCN]] |
** [[GoogLeNet|Inception]] (GoogLeNet) | ** [[GoogLeNet|Inception]] (GoogLeNet) | ||
** [[fully convolutional networks]] | ** [[fully convolutional networks]] | ||
2017년 8월 7일 (월) 23:54 판
by themes
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
- dl with torch
- bias 붙여버리면 inv는 어케 구하나? D=0되지 않나? 구할필요 없나?
- pytorch examples
- Identity Mappings in Deep Residual Networks arXiv:1603.05027
- cutting edge deeplearning for coders
general
- CUDA기타설치
- nets
- Learning to learn by GD by GD
- Generative Models (GAN, VAE, etc)
- Batch Normalization
- Mean Average Precision
- Essential Cheat Sheets for Machine Learning and Deep Learning Engineers
- What is surrogate loss?
- Exponential Linear Unit
- Neural net이 working하지 않는 37가지 이유
- deconvolution
- Sparse coding
- MXNet Model Zoo