Fast.ai

Fast.ai is both a research organization and an open-source library that has democratized deep learning by making it accessible to practitioners and researchers worldwide. Founded by Jeremy Howard and Rachel Thomas, fast.ai takes a unique "top-down" teaching approach that gets you building and deploying AI models quickly, then progressively reveals the underlying theory and mathematics. This contrasts with traditional academic approaches that can take months before students build anything useful.

The fastai library sits on top of PyTorch and provides high-level components that dramatically simplify common deep learning tasks. With just a few lines of code, you can train state-of-the-art models for computer vision, natural language processing, tabular data analysis, and collaborative filtering. The library incorporates research-backed best practices and techniques, so even simple code produces excellent results. This makes it possible for developers without PhD-level expertise to build and deploy production-quality AI systems.

The free courses have trained over 600,000 students from more than 200 countries, many of whom have gone on to win Kaggle competitions, publish research papers, and deploy AI in production at major companies. The courses emphasize practical application and ethical considerations alongside technical skills, ensuring students understand not just how to build AI, but when and why to use it responsibly.

For practitioners and researchers, fast.ai provides pre-trained models that can be fine-tuned for specific tasks with remarkably little data, transfer learning techniques that leverage existing models for new problems, and a vibrant community forum where thousands of practitioners share knowledge and help each other solve real problems.

Core Features

• •High-level deep learning library - Simplified API that makes building state-of-the-art models require far less code than PyTorch alone
• •Pre-trained models - Access to powerful pre-trained models that can be fine-tuned for specific tasks with minimal data
• •Multiple domains - Built-in support for computer vision, NLP, tabular data, and collaborative filtering with domain-optimized techniques
• •Best practices built-in - Incorporates cutting-edge research and proven techniques so even simple code produces excellent results
• •Free comprehensive courses - World-class deep learning courses available free online with practical, code-first teaching approach
• •Transfer learning focus - Leverage existing models for new tasks, dramatically reducing data requirements and training time
• •Extensive documentation - Clear documentation with practical examples and real-world applications
• •Active community - Vibrant forum with thousands of practitioners sharing knowledge and solving problems together

Who This Is For

Ideal for developers learning deep learning who want practical skills that lead to real applications quickly, data scientists building production ML systems without PhD-level expertise, researchers prototyping models and experimenting with new techniques rapidly, Kaggle competitors who need fast iteration and state-of-the-art techniques, businesses deploying AI solutions without large ML teams, and students who prefer hands-on learning over purely theoretical approaches.