일시 | Chapter | 발표자 | 발표/실습자료 | 발표영상 |
2017-01-10 | 1. Intro to Computer Vision, historical context. (week 1) | 모경현 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-01-12 | 2. Image classification and the data-driven approach k-nearest neighbor Linear classification I (week 2) | 서덕성 | 발표자료 실습자료 |
" target="_blank" rel="noopener">발표영상 |
2017-01-16 | 3. Linear classification II Higher-level representations, image features Optimization, stochastic gradient descent (week 3) | 김창엽 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-01-18 | 4. Backpropagation Introduction to neural networks (week 4) | 김해동 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-01-24 | 5. Training Neural Networks Part 1 (week 5) | 김준홍 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-01-26 | 6. Training Neural Networks Part 2 (week 6) | 김준홍 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-01-31 | 7. Convolutional Neural Networks (week 7) | 김보섭 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-02-02 | 8. ConvNets for spatial localization Object detection (week 8) | 박민식 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-02-07 | 9. Understanding and visualizing Convolutional Neural Networks Backprop into image (week 9) | 김동화 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-02-09 | 10. Recurrent Neural Networks (week 10) | 이기창 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-02-13 | 11. Training ConvNets in practice (week 11) | 서덕성 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
– | Overview of Caffe/Torch/Theano/TensorFlow (week 12) | Skip | ||
2017-02-24 | 12. Segmentation, Soft attention models, Spatial transformer networks (week 13) | 박재선 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
2017-03-03 | 13. ConvNets for videos Unsupervised learning (week 14) | 김동화 | 발표자료 | " target="_blank" rel="noopener">발표영상 |
– | Invited Talk by Jeff Dean (week 15) | Skip |