fast.ai
What it is: High-level deep learning library built on PyTorch that makes training state-of-the-art models accessible with minimal code.
What It Does Best
Best practices by default. Learning rate finding, discriminative learning rates, gradual unfreezing, and other advanced techniques work automatically. No need to implement them yourself.
Transfer learning made easy. Pre-trained models for vision, text, and tabular data. Fine-tune with one line of code and achieve state-of-the-art results.
Beginner-friendly power. Simple API for beginners but exposes PyTorch underneath for experts. Start simple, go deep when needed.
Key Features
Transfer learning: Pre-trained models for vision, NLP, tabular
Learning rate finder: Automatically find optimal learning rates
Mixed precision: Faster training with automatic FP16
Data augmentation: Built-in transforms and augmentations
Callbacks: Extensible training loop with powerful callbacks
Pricing
Free: Open source (Apache 2.0 license)
Course: Free online course (Practical Deep Learning for Coders)
Commercial: No licensing costs for any use
When to Use It
✅ Learning deep learning from scratch
✅ Need quick prototypes with great results
✅ Transfer learning for vision or NLP tasks
✅ Want PyTorch power with easier API
✅ Building practical applications quickly
When NOT to Use It
❌ Need full control over every training detail
❌ Working with custom architectures not supported
❌ Production systems requiring TensorFlow
❌ Very specialized research requiring low-level access
❌ Team already expert in raw PyTorch
Common Use Cases
Image classification: Medical imaging, product categorization
Object detection: Visual inspection, autonomous systems
NLP tasks: Text classification, sentiment analysis
Tabular data: Structured data prediction with deep learning
Recommendation systems: Collaborative filtering with neural networks
fast.ai vs Alternatives
vs PyTorch: fast.ai higher-level and easier, PyTorch more flexible
vs Keras: fast.ai better for research, Keras simpler for production
vs TensorFlow: fast.ai faster to prototype, TensorFlow better ecosystem
Unique Strengths
Best practices built-in: Advanced techniques work automatically
Learning resources: Excellent free course and community
PyTorch foundation: Access full PyTorch when needed
Research-proven: Techniques from cutting-edge research papers
Bottom line: Best library for learning deep learning or rapidly prototyping models. Combines ease of use with state-of-the-art techniques. Perfect for practitioners who want results fast without sacrificing quality. Not as flexible as raw PyTorch but far more productive for common tasks.