"Machine Learning"의 두 판 사이의 차이
ph
잔글 (→ril) |
잔글 (→general) |
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| 35번째 줄: | 35번째 줄: | ||
* [[Generative Models]] (GAN, VAE, etc) | * [[Generative Models]] (GAN, VAE, etc) | ||
* [[Batch Normalization]] | * [[Batch Normalization]] | ||
| + | * [[Fast(er) rcnn]] | ||
* [[Mean Average Precision]] | * [[Mean Average Precision]] | ||
* [https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5 Essential Cheat Sheets for Machine Learning and Deep Learning Engineers] | * [https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5 Essential Cheat Sheets for Machine Learning and Deep Learning Engineers] | ||
2017년 8월 4일 (금) 14:13 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
- Fast(er) rcnn
- 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