Welcome to my notes on computer vision.

Content I used to learn this material: Stanford’s CS231N (2023), relevant lectures from efficientml.ai (2023), and UMichs’s EECS 498 (2022)

the content

I’d recommend reading in order of the files, but I’ve tried to make the information as atomic as possible- enjoy!

In order to udnerstand the key architectures that underpine computer vision, I would advise you to review the fundamental ML section.

Applications

1. Semantic Segmentation
2. Object Detection
3. Instance Segmentation
4. 3D
5. Video Understanding
6. Visualization and understanding
7. Self-Supervised Learning
8. Generative Models

Practical Learning