Updated October 24, 2026
| Name | Fast.ai |
| Overview | Fast.ai offers practical deep learning courses and open-source learning resources. Its fastai Python library builds on PyTorch; PyTorch is a separate project, not a fast.ai product. The courses combine working examples with explanations of machine learning methods and data ethics. |
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| Who uses? |
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| Pricing | Practical Deep Learning for Coders is free to access, and its book is available to read online. This is not a freemium subscription product. Check any third-party notebook or hosting service separately for its current compute limits and charges. |
| Tags | Deep Learning, AI, Education, Open Source, Ethical AI |
| App available? | Courses and documentation are available on the web. Coding exercises use notebooks or a local Python environment, rather than a standalone consumer AI app. |
How to start learning with fast.ai
Begin with Practical Deep Learning for Coders Part 1: the 2022 course has nine lessons of roughly 90 minutes each. It assumes some coding experience, preferably Python, and high-school mathematics. Follow the first notebook, run its example, then change one input or dataset and inspect the result before moving on. Keep a separate validation set so a promising training result is not mistaken for performance on new data.
The course and fastai library are different resources: use lessons for the learning sequence and the library documentation to resolve API or installation questions. Record your package versions when revisiting older notebooks.
Official sources: Practical Deep Learning for Coders; fastai documentation.
