"Machine Learning"의 두 판 사이의 차이
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잔글 (→Recommendation) |
잔글 (→general) |
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| 23번째 줄: | 23번째 줄: | ||
** [[fully convolutional networks]] | ** [[fully convolutional networks]] | ||
* [[Learning to learn by GD by GD]] | * [[Learning to learn by GD by GD]] | ||
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* [[Generative Models]] | * [[Generative Models]] | ||
* [[Batch Normalization]] | * [[Batch Normalization]] | ||
* [[YOLO]] | * [[YOLO]] | ||
* [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 7월 17일 (월) 14:39 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 2016