"Machine Learning"의 두 판 사이의 차이
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잔글 (→general) |
잔글 (→general) |
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| 37번째 줄: | 37번째 줄: | ||
* [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] | ||
* [http://fa.bianp.net/blog/2014/surrogate-loss-functions-in-machine-learning/ What is surrogate loss?] | * [http://fa.bianp.net/blog/2014/surrogate-loss-functions-in-machine-learning/ What is surrogate loss?] | ||
| + | * [[Exponential Linear Unit]] | ||
2017년 7월 25일 (화) 22:42 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