Stanford CS231n: Deep Learning for Computer Vision

Course Website

• Course Website

Course Overview

This course is associated with Fei-Fei Li and is one of the most famous deep learning for computer vision courses in the world. In comparison to the UMich EECS 498-007 / 598-005 course, I found that some CS231n slides regarding 3DGS and YOLO are more up-to-date.

Ultimately, even though I learned computer vision mostly through the UMich course, CS231n is still an excellent course for anyone wanting an introduction to the computer vision world. It won’t disappoint you.

My Learning Journey

• 2025.8.23 Lecture 1 Introduction
• 2025.8.24 Lecture 2 Image Classification with Linear Classifiers
• 2025.8.26 Lecture 3 Regularization and Optimization
• 2025.8.28 Lecture 4 Neural Networks and Backpropagation
• 2025.9.7 Lecture 5 Image Classification with CNNs
• 2025.10.17 Lecture 6 CNN Architectures
• 2025.10.21 Lecture 7 Recurrent Neural Networks
• 2025.10.22 Lecture 8 Attention and Transformers
• 2025.10.22 Lecture 9 Object Detection, Image Segmentation, Visualizing and Understanding
• 2025.10.23 Lecture 10 Video Understanding
• 2025.10.23 Lecture 11 Large Scale Distributed Training
• 2025.10.24 Lecture 12 Self-supervised Learning
• 2025.10.27 Lecture 13 Generative Models 1
• 2025.10.27 Lecture 14 Generative Models 2
• 2025.10.28 Lecture 15 3D Vision
• 2025.10.29 Lecture 16 Vision and Language
• 2025.10.29 Lecture 17 Robot Learning