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