fastbook
The fastai book, published as Jupyter Notebooks, provides an introduction to deep learning, fastai, and PyTorch.
What is fastbook?
The fastai book is an open-source educational resource that teaches deep learning through interactive Jupyter Notebooks. It provides a practical introduction to the fastai library and PyTorch, covering fundamental concepts and real-world applications across computer vision, NLP, and tabular data. The material serves as the basis for both a popular MOOC and a published book.
Developers and coders who want to learn deep learning without an advanced mathematics background, particularly those interested in practical AI applications using fastai and PyTorch.
It offers a hands-on, code-first approach to learning deep learning with professionally structured content that's immediately executable. The integration with Google Colab removes setup barriers, making it one of the most accessible entry points into modern AI education.
Overview
The fastai book, published as Jupyter Notebooks
Use Cases
Best For
- Learning deep learning fundamentals through interactive coding
- Getting started with the fastai library and PyTorch
- Following along with the fast.ai MOOC curriculum
- Exploring practical AI applications in computer vision and NLP
- Studying deep learning without local environment setup
- Understanding modern deep learning architectures and techniques
Not Ideal For
- Researchers or students needing in-depth mathematical theory and proofs, as the focus is on practical application over theoretical rigor.
- Developers seeking production deployment tutorials or enterprise-grade code, since the content is educational and not optimized for deployment scenarios.
- Learners who prefer passive consumption via videos or textbooks without interactive coding, as all material requires active engagement with Jupyter Notebooks.
- Projects based on TensorFlow or other non-PyTorch frameworks, due to its tight integration with fastai and PyTorch.
Pros & Cons
Pros
Every chapter is a Jupyter Notebook that can be run and modified in real-time, facilitating hands-on learning and experimentation directly in the browser.
Seamless integration with Google Colab allows users to start learning immediately without local environment configuration, lowering the barrier to entry for beginners.
Covers essential deep learning domains including computer vision, NLP, and tabular data, with progression from basics to advanced architectures like ResNet and optimizers.
README and resources are available in multiple languages such as Spanish, Korean, and Chinese, making the material accessible to a wider international audience.
Cons
The non-code prose is not licensed for redistribution or commercial use, which can hinder community adaptations, sharing, and derivative works beyond personal use.
Heavy reliance on Google Colab for the recommended experience means learning is dependent on a third-party service's availability, policies, and potential downtime.
The curriculum is deeply tied to the fastai library, which might not fully prepare learners for working directly with vanilla PyTorch or other frameworks in production.
Frequently Asked Questions
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