Standford University의 CS231n: Convolutional Neural Networks for Visual Recognition (Spring 2017)강의를 들으면서 공부한 내용을 블로그에 포스팅할 계획이다.
참고한 자료 ⛓️💥
1. CS231n 강의 동영상
https://youtube.com/playlist?list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk&si=KHBmWOifxG5cmhIi
Stanford University CS231n, Spring 2017
CS231n: Convolutional Neural Networks for Visual Recognition Spring 2017 http://cs231n.stanford.edu/
www.youtube.com
2. 공식 GitHub
:과제가 올라와 있어서, 최대한 풀면서 넘어가려고 한다.
https://github.com/cs231n/cs231n.github.io
GitHub - cs231n/cs231n.github.io: Public facing notes page
Public facing notes page. Contribute to cs231n/cs231n.github.io development by creating an account on GitHub.
github.com
3. 한국어 자막
: 최대한 자막 없이 영어 그대로를 들을 예정이다.
https://github.com/visionNoob/CS231N_17_KOR_SUB
GitHub - visionNoob/CS231N_17_KOR_SUB: CS231N 2017 video subtitles translation project for Korean Computer Science students
CS231N 2017 video subtitles translation project for Korean Computer Science students - visionNoob/CS231N_17_KOR_SUB
github.com
계획표 🌏
정리하는대로 업데이트 할 예정
| No | Lecture | URL |
| 1 | Introduction to Convolutional Neural Networks for Visual Recognition | |
| 2 | Image Classification | |
| 3 | Loss Functions and Optimization | |
| 4 | Introduction to Neural Networks | |
| 5 | Convolutional Neural Networks | |
| 6 | Training Neural Networks I | |
| 7 | Training Neural Networks II | |
| 8 | Deep Learning Software | |
| 9 | CNN Architectures | |
| 10 | Recurrent Neural Networks | |
| 11 | Detection and Segmentation | |
| 12 | Visualizing and Understanding | |
| 13 | Generative Models | |
| 14 | Deep Reinforcement Learning |
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