> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/course/cs231n.md).

# CS231n Resources

* [CS231n Main Site](http://cs231n.stanford.edu)
* [CS231n Github Page](http://cs231n.github.io/)
* [CS231n GIthub Source](https://github.com/cs231n/cs231n.github.io)
* [CS231n Schedule](http://cs231n.stanford.edu/schedule.html)
* [CS231n Slides](http://cs231n.stanford.edu/slides/)
* [CS231n YouTube](https://www.youtube.com/playlist?list=PLLvH2FwAQhnpj1WEB-jHmPuUeQ8mX-XXG)
* [CS231n Twitter](https://twitter.com/cs231n)
* [CS231n Korean](http://aikorea.org/cs231n/)

## How To

* [Set Up Google Cloud Platform for Machine Learning](https://github.com/cs231n/gcloud)

## Student Notes

* [albertpumarola/deep-learning-notes: My CS231n lecture notes](https://github.com/albertpumarola/deep-learning-notes)
* [hnarayanan/CS231n: Working through CS231n: Convolutional Neural Networks for Visual Recognition](https://github.com/hnarayanan/CS231n)
* [mbadry1/CS231n-2017-Summary](https://github.com/mbadry1/CS231n-2017-Summary)
* [visionNoob/CS231N\_17\_KOR\_SUB: CS231N 2017 video subtitles translation project for Korean Computer Science students](https://github.com/visionNoob/CS231N_17_KOR_SUB)
* [Yorko/stanford\_cs231n\_2019: Solutions and comments to assignments for 2019 Stanford's course on convolutional neural networks](https://github.com/Yorko/stanford_cs231n_2019)
* [maxis42/CS231n: Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition](https://github.com/maxis42/CS231n)
* [LoserSun/cs231n-study-schedule: cs231n learning notes](https://github.com/LoserSun/cs231n-study-schedule)
* [zhuole1025/cs231n: notes & assignments for cs231n 2020](https://github.com/zhuole1025/cs231n)
* [khanhnamle1994/computer-vision: Programming Assignments and Lectures for Stanford's CS 231: Convolutional Neural Networks for Visual Recognition](https://github.com/khanhnamle1994/computer-vision)

## Student Forums

* [Stanford CS231n: Convolutional Neural Networks for Visual Recognition](https://www.teddit.net/r/cs231n/)

## CS231 Assignment

* [dengfy/cs231n: Convolutional Neural Networks for Visual Recognition in Stanford](https://github.com/dengfy/cs231n)
* [lightaime/cs231n: cs231n assignments sovled by https://ghli.org](https://github.com/lightaime/cs231n)
* [Burton2000/CS231n-2017: Completed the CS231n 2017 spring assignments from Stanford university](https://github.com/Burton2000/CS231n-2017)

## Similar with CS231n

* [CS294-129 Designing, Visualizing and Understanding Deep Neural Networks, University of California Berkeley](https://bcourses.berkeley.edu/courses/1453965/), license Public Domain : pptx
  * [CAL ESG - EECS - YouTube](https://www.youtube.com/user/esgeecs/live)
* [EECS 498-007 / 598-005: Deep Learning for Computer Vision](https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/schedule.html)
  * [Deep Learning for Computer Vision - YouTube](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)
* [CS 6501 Deep Learning for Visual Recognition](https://www.vicenteordonez.com/deeplearning/) : pptx
* [CS 6501 - 009 Computational Visual Recognition](http://www.cs.virginia.edu/~vicente/recognition/)
* [CSE 576 Computer Vision Spring 2020](https://courses.cs.washington.edu/courses/cse576/20sp/calendar/) : pptx
* [EECS 442: Computer Vision](https://web.eecs.umich.edu/~justincj/teaching/eecs442/WI2021/schedule.html) : pptx
* [ECE 6504 Deep Learning for Perception](https://computing.ece.vt.edu/~f15ece6504/), license free : pptx
* [Machine Learning](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
* [CSCI 1430: Introduction to Computer Vision](https://browncsci1430.github.io/webpage/index.html) license free : pptx
* [Deep Learning in Data Science](https://kth.instructure.com/courses/17088/pages/lectures)
* [Mathematical Statistics](http://people.uncw.edu/chenc/STT592_Deep%20Learning/STT592DeepLearning_Index.html)
* [Deep learning CS342 UT Austin](https://www.philkr.net/cs342/material), license CC-BY-SA;
* [CS 498 DL University Illinois](https://slazebni.cs.illinois.edu/spring21/)
* [CAP 5636 - Advanced Artificial Intelligence](https://www.eecs.ucf.edu/~lboloni/Teaching/CAP5636_Fall2020/)
  * [Index of /\~lboloni/Teaching/CAP5636\_Fall2020/slides](https://www.eecs.ucf.edu/~lboloni/Teaching/CAP5636_Fall2020/slides/)
* [Stanford University: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/syllabus.html)
* [ECE 5973-961/983: Artificial Neural Networks and Applications](https://samuelcheng.info/deeplearning_2018/)
* [ECE 6504 Deep Learning for Perception](https://computing.ece.vt.edu/~f15ece6504/#present) ppt license: CC-BY
* [Intro to Machine Learning - ECE, Virginia Tech - Spring 2015: ECE 5984](https://computing.ece.vt.edu/~s15ece5984/) ppt license: CC-BY
* [Introduction to Deep Learning @CUHK](http://dl.ee.cuhk.edu.hk/)
* [CS 7643 Deep Learning](https://www.cc.gatech.edu/classes/AY2021/cs7643_fall/) ppt license: CC-BY
* [Yaoliang Yu](https://cs.uwaterloo.ca/~y328yu/mycourses/480-2020/lecture.html) ppt
* [CS 165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2019.html) ppt

## EECS498 Student Notes

* [linxiaow/EECS498-Deep-Learning-for-Vision](https://github.com/linxiaow/EECS498-Deep-Learning-for-Vision)
* [hsdong2012/eecs498\_assignments: eecs498\_assignments](https://github.com/hsdong2012/eecs498_assignments)

## CS294-129 Student Notes

* [kavimaluskam/cs294-129-exercise](https://github.com/kavimaluskam/cs294-129-exercise)
* [arjasethan1/cs294-129: CS294-129 Designing, Visualizing and Understanding Deep Neural Networks](https://github.com/arjasethan1/cs294-129)

## CS498 DL Student Notes

* [Yuting1007/CS498DL](https://github.com/Yuting1007/CS498DL)
* [yutong-xie/CS498-DL-Assignment: Assignment for CS498 Deep Learning FA20 UIUC](https://github.com/yutong-xie/CS498-DL-Assignment)

## Related Books

* [Computer Vision: Algorithms and Applications, 2nd ed.](http://szeliski.org/Book/)