"Machine Learning"의 두 판 사이의 차이
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2018년 11월 13일 (화) 17:57 판
by themes
posts
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
nets
- CNN
- AlexNet
- dilated cnn
- pathnet
- ResNet
- Fast RCNN
- Faster RCNN
- R-FCN
- Inception (GoogLeNet)
- fully convolutional networks
- FractalNets
- highway networks
- Memory networks
- DenseNet
- NIN
- DSN
- Ladder Networks
- DFNs
- YOLO
general
- Affinity Propagation, Apcluster sparse
- CUDA기타설치
- 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
- logistic regression
- information bottleneck
- Artificial Curiosity
- sklearn examples