Machine Learning

after AI

• cot연구동향

by themes

• Recommendation
• Face
• Person re-identification
• segmentation

posts

• conv1d
• naive gradient descent
• nll loss

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
  • sklearn preprocessing

x

• Isolation Forest