CS231n Study

This repository contains my study and summaries of Stanford University's 'Convolutional Neural Networks for Visual Recognition' (CS231n) course.

Course Overview

CS231n is a course that focuses on computer vision and deep learning. In this course, we learn how to use Convolutional Neural Networks (CNNs) to solve various problems such as image classification, object detection, and image generation.

Contents

• Lecture Notes: These are summaries of each lecture, capturing the key concepts.
• Assignments: This includes the code for assignments along with explanations.
• Project: This contains the code and results developed in the final project.

Environment Setup

Python 3.x along with the following packages must be installed:

• numpy
• matplotlib
• scipy
• scikit-image
• opencv-python
• tensorflow or pytorch

You can install them using the following command:

pip install numpy matplotlib scipy scikit-image opencv-python tensorflow 

or

pip install numpy matplotlib scipy scikit-image opencv-python torch torchvision torchaudio

How to Use This Repository

The files inside each Lecture Notes and Assignments folder contain code and text related to what was learned in that week. These materials can be used for review and practice of the CS231n course content.

Lecture materials

CS231n: http://cs231n.stanford.edu/slides/2021/