Fastbook
Fastbook is the official GitHub repository containing the full set of Jupyter notebooks for the book "Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD" by Jeremy Howard and Sylvain Gugger, published by O'Reilly Media. The notebooks mirror the chapters of the printed book, walking readers from training a state-of-the-art image classifier in the first chapter through to deeper topics such as stochastic gradient descent from scratch, neural network foundations, NLP, tabular data, collaborative filtering, and model deployment. Each notebook is self-contained and runnable in Jupyter, Google Colab, or Kaggle Notebooks, and relies on the fastai library built on top of PyTorch. The repository is intended as a practical, code-first learning resource: readers execute and modify the code rather than only reading theory. Markdown cells in the notebooks summarize explanation and point back to the corresponding book chapters. The project is maintained by the fastai organization and is released as open source, with the notebooks freely available on GitHub alongside the commercial book. It is frequently used in study groups, university courses, and self-directed machine learning curricula, and the community files corrections and improvements via issues and pull requests. Because it is a teaching resource rather than a packaged library, Fastbook does not ship installable software of its own; users install the fastai library and PyTorch separately and then run the notebooks locally or in a hosted notebook environment.
- Category
- AI/ML
- Pricing
- Open Source
Problem solved
Fastbook addresses the difficulty of learning modern deep learning in a practical, top-down way. Many learners find theory-first courses hard to apply or lack GPU-ready, runnable code. Fastbook solves this by pairing a complete, freely available set of executable notebooks with an accompanying book, so readers can train real models on real datasets from the first chapter without needing a PhD-level mathematical background.
Use cases
- Following the fastai book chapter by chapter with runnable notebooks
- Self-study of practical deep learning with PyTorch and fastai
- Running the notebooks in Google Colab or Kaggle without a local GPU
- Teaching or running a university course or study group on deep learning
- Reference implementations for image classification, NLP, tabular, and recommender models
- Transferring and fine-tuning pretrained models on custom datasets
- Learning gradient descent and neural network internals from scratch
Audience
- Developers
- Data Scientists
- ML Engineers
- Researchers
- Hobbyists
Pros
- Free, open-source companion notebooks to a well-regarded deep learning book
- Top-down teaching approach that gets readers training models immediately
- Runnable in hosted environments such as Colab and Kaggle, avoiding local setup friction
- Covers a broad range of domains: vision, NLP, tabular data, and recommender systems
- Actively used by a large community with issues and pull requests for fixes
Cons
- Not a standalone library or tool; requires fastai and PyTorch to be installed separately
- Tied to the fastai/PyTorch ecosystem and specific library versions, so code can drift as APIs change
- Requires GPU access for comfortable training on most chapters
- Content is oriented toward learning rather than production deployment patterns
- Notebooks are documentation-like, so there is no formal release/versioning for consumers
Official website: https://github.com/fastai/fastbook
Canonical LambdaBase page: https://www.lambdabase.com/tools/fastbook