This project is set up for experimenting with and running notebooks from the fast.ai Practical Deep Learning for Coders course.
This project uses uv for fast and reliable Python package management.
-
Install uv if you haven't already:
curl -LsSf https://astral.sh/uv/install.sh | sh -
Install project dependencies:
uv sync
The project includes all necessary dependencies for the fast.ai course:
- Jupyter Lab/Notebook - For running interactive notebooks
- fastai - The main fast.ai library with PyTorch backend
- matplotlib - For plotting and visualization
- pandas, numpy, scikit-learn - Core data science libraries
- torch, torchvision - PyTorch deep learning framework
- duckduckgo_search - For image search in computer vision notebooks
- pillow - Python Imaging Library for image processing
- fastcore, fastdownload - Utilities for fastai
uv run jupyter labThis will start Jupyter Lab and open it in your browser. Navigate to the notebooks/ directory to access course notebooks.
uv run jupyter notebookpractical-deep-learning-for-coders/
├── notebooks/ # Course notebooks organized by lesson
│ ├── self/ # Personal exploratory notebooks
│ │ └── fastai-intro.ipynb
│ ├── kaggle/ # Notebooks from Kaggle sources
│ │ ├── lesson01/ # Lesson 1: Jupyter Notebook basics
│ │ │ ├── jupyter-notebook-101.ipynb
│ │ │ └── is-it-a-bird-creating-a-model-from-your-own-data.ipynb
│ └── README.md # Notebooks organization guide
├── pyproject.toml # Project configuration and dependencies
├── uv.lock # Locked dependency versions
└── README.md # This file
When you download new notebooks from the course:
-
Create a new lesson directory:
mkdir notebooks/lessonXX
-
Download the notebook from Kaggle and place it in the lesson directory
-
Update the notebooks README.md with the new lesson information
- Add new dependencies:
uv add <package> - Remove dependencies:
uv remove <package> - Update lock file:
uv lock - Run Python scripts:
uv run <script.py>