Lab Notebooks

The labs are Jupyter notebooks, one per lecture. Every one runs on CPU alone and works offline once its data is bundled — no GPU required, no API key, no paid service. Run them in the cloud with a single click, or locally if you prefer your own machine. In practice the deep-learning and reinforcement-learning labs train noticeably faster on a GPU, so use one if you have access.

▶  Open in GitHub Codespaces Browse the repository

Option 1 — run in the cloud (Codespaces)

• Click Open in GitHub Codespaces. GitHub builds the environment from .devcontainer/devcontainer.json and installs everything in requirements.txt automatically (first launch takes a few minutes).
• When VS Code opens in your browser, use the file explorer to open the notebook you want under the labs/ folder, then run the cells.
• When you finish, Stop or Delete the codespace (github.com/codespaces) so it doesn't keep using your quota.

Option 2 — run locally

Python 3.11 or newer, then:

git clone https://github.com/vonzhg/AI-ECON-2026.git
cd AI-ECON-2026
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
jupyter lab

requirements.txt pins the CPU build of PyTorch, so the install stays small. If you would rather not install anything, do the setup lab for Lectures 1–2 first — it walks through VS Code, Python, and PyTorch step by step.

Notebooks