How to Run Jupyter Notebooks in the AI for Beginners Project: A Complete Setup Guide

To run the Jupyter notebooks in Microsoft's AI for Beginners course, clone the repository using either a full or sparse checkout, create a Conda environment from the provided environment.yml, install Jupyter, and launch the server with jupyter notebook.

The microsoft/AI-For-Beginners repository delivers a comprehensive, hands-on curriculum for artificial intelligence fundamentals through interactive Jupyter notebooks. To run these notebooks locally, you need to configure a Python environment with the specific dependencies defined in the project's configuration files. This guide walks you through the exact steps to clone the repository, set up your environment, and start exploring the lessons.

Clone the Repository (Full vs. Sparse Checkout)

The README at the repository root offers two methods for obtaining the codebase. The full clone downloads all 50+ translation files, while the sparse checkout skips these large folders for a faster, leaner download.

Full Clone Option

If you want the complete repository including all translations, use the standard clone command:

git clone https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners

Sparse Checkout (Recommended)

For beginners who want to save disk space and download time, Microsoft recommends a sparse checkout that excludes the translation directories:

git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

Set Up the Python Environment

The project includes an environment.yml file that pins all required packages, including Jupyter, PyTorch, OpenCV, and scientific computing libraries.

Using Conda (Recommended)

Create and activate the environment using the provided configuration:

conda env create -f environment.yml
conda activate ai4beg

Using pip and virtualenv

If you prefer pip, install the dependencies listed in requirements.txt:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

pip install -r requirements.txt

Install and Launch Jupyter

While the Conda environment includes Jupyter, verify installation or add it manually if using pip.

Installing Jupyter

pip install notebook   # Or: conda install notebook

Starting the Server

Launch Jupyter from the repository root. The server will start a local web server and print a URL to your terminal:

jupyter notebook

For a more modern interface, use JupyterLab:

jupyter lab

Navigate and Run Lesson Notebooks

Once the browser opens, navigate to the lessons/ directory to access the curriculum. Each module contains interactive .ipynb files with executable code and explanations.

For example, to study the Perceptron implementation, open:

lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb

Or to explore convolutional neural networks:

lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb

Each lesson directory includes a README describing learning objectives and any required dataset downloads. The lessons/0-course-setup/how-to-run.md file provides additional guidance specific to the development environment.

Troubleshooting Common Issues

If you encounter errors launching notebooks, consult troubleshoot.md in the repository root. This document addresses common problems such as missing Jupyter installations, kernel connection failures, and package version conflicts. The troubleshooting guide specifically recommends verifying your Jupyter installation with pip install notebook if the command is not found.

Summary

• Clone the repository using either a full clone or a sparse checkout to exclude translations and save space.
• Create the Python environment using conda env create -f environment.yml or manually install packages via pip install -r requirements.txt.
• Install Jupyter Notebook or JupyterLab using pip or conda.
• Launch the server with jupyter notebook and navigate to specific lesson files in the lessons/ directory.
• Reference troubleshoot.md for solutions to common setup issues.

Frequently Asked Questions

Do I need a GPU to run the notebooks?

No, the majority of the AI for Beginners notebooks run comfortably on CPU-only machines. While some deep learning lessons in lessons/4-ComputerVision/ may train faster with GPU acceleration, the code defaults to CPU execution and includes fallbacks for systems without CUDA.

Can I run these notebooks in Google Colab?

Yes, you can upload the .ipynb files to Google Colab or Azure Notebooks. However, you may need to manually install dependencies listed in requirements.txt at the start of each session, as the cloud environments do not automatically load the course's Conda environment.

What if Jupyter doesn't open in my browser?

If the terminal shows the server URL but your browser doesn't launch automatically, copy the URL (typically http://localhost:8888/ with a token) and paste it manually into your browser. Ensure no firewall is blocking the local port, and check troubleshoot.md for platform-specific browser integration issues.

How do I update the environment when the course changes?

When the repository updates, pull the latest changes and update your environment. For Conda users, run conda env update -f environment.yml after pulling. For pip users, run pip install -r requirements.txt --upgrade to refresh the package versions.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}