Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.
Course Homepage: CS231n: Convolutional Neural Networks for Visual Recognition
Lecture Online Notes: CS231n Lecture Notes
Annual Slides: CS231n slides
- Assignment 1: Deep Learning Basics
- Assignment 2: Perceiving and Understanding the Visual World
- Assignment 3: Generative and Interactive Visual Intelligence
本仓库主要包含 spr2024 版的课程 slides、spr2023 版的 3 个 assignments + solutions 等，其中大部分 solutions 是在本地 3050ti 的显卡上跑的，也有的是在 Google Colab TPU 上跑的。
（ps1: CS231n 的 课程在线笔记 总结的很有意思也很细致，对课程学习很有帮助；也可参考 智能单元专栏 ）
（ps2: 还存了一些 discussions、Guest Lectures 等）