PDFAITools
fast.ai
Free practical deep learning course. Top-down approach that gets you building real models from day one.
What is fast.ai?
Practical Deep Learning for Everyone
fast.ai is a free, open-source deep learning course and library developed by Jeremy Howard and Rachel Thomas. It takes a top-down, application-first approach to teaching deep learning — you build real, state-of-the-art models from the very first lesson, and then progressively learn the underlying theory. This approach is highly effective for practitioners who want to apply deep learning quickly.
Philosophy and Approach
Most deep learning courses teach theory first and applications later, which can be discouraging before learners see real results. fast.ai flips this — you train a computer vision classifier or NLP model in the first hour, then dive progressively deeper into the math and architecture. The library, built on PyTorch, provides high-level abstractions that enable rapid experimentation without sacrificing low-level access.
- Free, practical deep learning course (Practical Deep Learning for Coders)
- fastai Python library built on PyTorch
- Applications-first pedagogical approach
- Covers computer vision, NLP, tabular data, and collaborative filtering
Key Features
Build working deep learning models from lesson one, with theory following naturally from practical use.
Practical Deep Learning for Coders is freely available online with no registration or payment required.
High-level PyTorch wrapper that enables training state-of-the-art models with minimal code.
Large, active forum and community of practitioners sharing projects, questions, and insights.
Part 1 teaches applications; Part 2 goes deep into model internals and novel research approaches.
Who Uses fast.ai?
Build image classifiers, object detectors, and segmentation models using transfer learning.
Train text classifiers, language models, and sequence models using the fastai NLP modules.
Apply deep learning to structured tabular data alongside traditional ML approaches.
Part 2 of the course teaches how to implement novel architectures and reproduce research papers.
Pros & Cons
✅ Pros
- Completely free — no course fees, no subscription, no paywall
- Top-down approach gets learners building real models immediately
- fastai library is powerful and widely used in the ML community
- Jeremy Howard is an exceptionally clear teacher with deep practical experience
- Strong community support through active forums and study groups
❌ Cons
- Assumes Python programming knowledge — not suitable for absolute beginners
- Top-down approach can leave gaps in theoretical understanding if not supplemented
- Less structured than Coursera courses — self-discipline required
- fastai library is less commonly used in enterprise environments than raw PyTorch/TF
fast.ai Pricing
Free
- Full course access
- fastai library
- Forums
- All materials
- No registration required
fast.ai earns a 4.4/5 rating from our editorial team. It's completely free to use with no hidden costs, making it one of the most accessible tools in the Education & Learning space. Standout strengths include completely free — no course fees, no subscription, no paywall and top-down approach gets learners building real models immediately.
Get Started with fast.ai →