Passing cs231n together within the OpenDataScience community
Main links
- The course itself
- Video-lectures, youtube channel. Prerequisites are given in the 1st lecture
- Syllabus with assignments
Assignments
There are 3 big and tough assignments in this course. We’ll have deadlines and exemplar solutions (by me or smb else) to be discussed.
Competitions & projects
In the original course they've got projects. You can also complete one, but actually, lectures and assignments is already a good workload. To do smth more, I propose 3 variants:
- a personal pet-project (will be nice to show in your portfolio) here is an example by @artgor and a description in 🇷🇺 (you can translate it, but the app is self-explanatory)
- a Kaggle competition. Maybe a playground one, to start with. This one, persay: "Dog Breed Identification"
- you can also write a tutorial
GPUs
Authors claim that you can pass the course even with typical hardware. However, I recommend to rent a machine with GPU. The most convenient option right now is either Google Colaboratory (tutorial on Medium) or even Kaggle Kernels (just switch on GPU in Kernel settings).
Plan
- 09.01.19 – 15.01.19. Lecture 1 and Lecture 2, Assignment 1 announced
- 16.01.19 – 22.01.19. Lecture 3 and Lecture 4
- 23.01.19 – 29.01.19. Lecture 5 and Lecture 6
- 30.01.19 – A1 due. You can upload your solution here
- 30.01.19 – 05.02.19. Lecture 7 and Lecture 8, Assignment 2 announced
- 06.02.19 – 12.02.19. Lecture 9 and Lecture 10
- 13.02.19 – 19.02.19. Lecture 11 and Lecture 12
- 20.02.19 – A2 due. You can upload your solution here
- 20.02.19 – 26.02.19. Lecture 13 and Lecture 14, Assignment 3 announced
- 27.02.19 – 05.03.19. Lecture 15 and Lecture 16
- 13.03.19 – A3 due. You can upload your solution here