[CS231n] Convolutional Neural Networks for Visual Recognition, Standford Univers

강의 자료

강의 공식 사이트 : https://cs231n.stanford.edu/index.html
Spring 2017 강의 on Youtube : https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv
Spring 2017 강의 프로젝트 리포트 : https://cs231n.stanford.edu/2017/reports.html
강의 노트 (Spring 2024 숙제 리스트, 사전지식 강의) : https://cs231n.github.io/
Github : https://github.com/cs231n/cs231n.github.io

번역 자료

영어 자막 한글 번역본 : https://github.com/visionNoob/CS231N_17_KOR_SUB
한국어 설명 동영상 : https://www.youtube.com/playlist?list=PL1Kb3QTCLIVtyOuMgyVgT-OeW0PYXl3j5

목차

Lecture	Description	Youtube	Slides	URL
1	Introduction to Convolutional Neural Networks for Visual Recognition	video	slide
2	Image Classification	video	slide
3	Loss Functions and Optimization	video	slide
4	Introduction to Neural Networks	video	slide
5	Convolutional Neural Networks	video	slide
6	Training Neural Networks I	video	slide
7	Training Neural Networks II	video	slide
8	Deep Learning Software	video	slide
9	CNN Architectures	video	slide
10	Recurrent Neural Networks	video	slide
11	Detection and Segmentation	video	slide
12	Visualizing and Understanding	video	slide
13	Generative Models	video	slide
14	Deep Reinforcement Learning	video	slide
Guest	Invited Talk: Song Han Efficient Methods and Hardware for Deep Learning	video	slide
Guest	Invited Talk: Ian Goodfellow Adversarial Examples and Adversarial Training	video	slide

References

• https://lynnshin.tistory.com/3