- k-Nearest Neighbor (kNN) exercise + comments on some derivations
- Softmax classifier
- Multi-class SVM
- Two-layer net
- More features
- Image captioning with RNNs
- Image captioning with LSTMs
- Network Visualization (PyTorch)
- Generative Adversarial Networks (PyTorch)
- Style Transfer (PyTorch)
Passing cs231n together within the OpenDataScience community
Next start - from 02.12.2019 till 08.03.2020
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
- 02.12.19 – 08.12.19. Lecture 1 and Lecture 2
- 09.12.19 – 15.12.19. Lecture 3 and Lecture 4
- 16.12.19 – 22.12.19. Lecture 5 and Lecture 6
- 22.12.19 – A1 due. You can discuss it in the #class_cs231n channel in Slack
- 23.12.19 – 12.01.20. Lecture 7 and Lecture 8
- 13.01.20 – 26.01.20. Lecture 9 and Lecture 10
- 27.01.20 – 09.02.20. Lecture 11 and Lecture 12
- 09.02.20 – A2 due. You can discuss it in the #class_cs231n channel in Slack
- 10.02.20 – 23.02.20. Lecture 13 and Lecture 14
- 24.02.20 – 08.03.20. Lecture 15 and Lecture 16
- 08.03.20 – A3 due. You can discuss it in the #class_cs231n channel in Slack