CS 231n GitHub serves as the primary public workspace for the Stanford CS 231n convolutional neural networks course, hosting reference implementations, starter code, and completed assignments. This guide explains how course resources are organized, how to set up the environment, and how to use the repository effectively for learning or teaching.
Repository Overview and Structure
The CS 231n GitHub organization typically contains repositories for each assignment, datasets, utilities, and lecture material. Each assignment repo is self-contained, providing a clear folder hierarchy for data, code, and tests.
Standard Repository Layout
Assignments follow a consistent pattern so contributors can quickly locate key files:
- assignment1/cs231n/classifiers: Python modules for models
- assignment1/cs231n/data: Datasets and pretrained weights
- assignment1/cs231n/util: Helper functions for preprocessing and visualization
- assignment1/tests: Unit tests and gradient checks
- assignment1/README.md: Instructions and deliverables
This predictable structure reduces cognitive load and supports reproducibility across semesters.
Setting Up Your Local Environment
Cloning the course repository and installing dependencies is straightforward when you follow the documented procedure. A reliable environment minimizes setup friction and lets you focus on algorithms.
- Fork the official course template to your GitHub account.
- Clone your fork locally: git clone https://github.com/YOUR_USERNAME/cs231n.git
- Create a virtual environment: python -m venv .venv
- Install dependencies: pip install -r requirements.txt
- Verify CUDA and cuDNN paths if using a GPU (optional but recommended).
After installation, run a small smoke test to confirm imports work before starting the assignment.
Workflow and Version Control Best Practices
Effective use of Git reduces merge conflicts and keeps your submissions clean. Adopt a lightweight branching model and commit incrementally with descriptive messages.
Recommended Git Workflow
- Create a feature branch per task (e.g., feat/knn-distance)
- Commit atomic changes with clear messages
- Rebase onto upstream main to sync without unnecessary merges
- Run linting and tests before pushing
- Push to your fork and open a draft PR for review
These practices mirror industry standards while keeping student workflows simple and traceable.
Common Assignments and Key Artifacts
CS 231n typically progresses from classic models to modern CNNs, with each assignment producing measurable artifacts you can inspect later.
| Assignment | Key Deliverable | Notes |
|---|---|---|
| Image Classification | Softmax + SVM training scripts | Baseline for all later work |
| Neural Networks | Two-layer and fully connected network | Backpropagation implementation validated |
| Convolutional Networks | CNN architectures and training logs | Explore regularization and optimizers |
| RNN Captioning | LSTM-based caption generator | Sequence modeling focus |
Each assignment repo documents expected outputs, acceptable accuracy ranges, and common pitfalls.
Debugging, Testing, and Performance Tips
Reliable experiments depend on deterministic testing and careful instrumentation. Use the provided test suite and gradient checking to catch regressions early.
- Run unit tests with pytest to catch shape mismatches
- Use numeric gradient checking for new layers
- Log training curves to detect overfitting or vanishing gradients
- Profile with simple timing hooks before optimizing
Small, incremental changes make debugging straightforward and reduce guesswork.
Collaboration and Academic Integrity
GitHub enables collaboration, but CS 231n enforces strict academic standards regarding individual work. Use your fork privately unless explicitly permitted for pair programming, and cite any external code snippets.
Review the syllabus and Honor Code policy before sharing solutions or discussing implementation details with peers outside approved channels.
Extending the Repository for Future Courses
The structure you build for CS 231n can serve as a template for future deep learning projects. Adopt documentation habits, experiment tracking, and clean module boundaries to scale complexity without losing clarity.
Keep your repos organized, versioned, and documented so that advancing from CS 231n to research or production pipelines is a smooth transition.