fast.ai

fast.ai: Making neural nets uncool again is a non-profit educational and research community focused on accessible deep learning, practical tooling, and ethical AI. The platform offers free courses, practical libraries, and thoughtful commentary spanning machine learning, education, health, society, and technology. Core themes include hands-on coding, accessible explanations of foundational ideas, and building end-to-end AI solutions that emphasize real-world impact over hype. The content is organized around courses, books, libraries, and a steady stream of research-backed insights from contributors like Jeremy Howard, Rachel Thomas, and the fast.ai community.

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Overview

• Free, code-first deep learning courses and tutorials (e.g., Practical Deep Learning for Coders).
• Open-source libraries and tooling to accelerate development (e.g., nbdev, fasttransform, fastai, fastdownload).
• Broad coverage including technical ML topics, AI ethics, education, and policy implications.
• A strong emphasis on practical deployment, reproducibility, and accessible education for a diverse audience.

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What You’ll Find

• Course content and schedules for Practical Deep Learning for Coders and related programs.
• Blog posts and essays on AI ethics, governance, education, and societal impacts of automation.
• Technical articles about new libraries, programming languages, and data processing pipelines.
• Announcements about collaborations, new labs (Answer.AI), and shifts in educational format.

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How It Works

• Learn by doing: work through chapters and notebooks that teach concepts through hands-on coding with modern DL frameworks.
• Build end-to-end projects and templates that demonstrate practical use cases across industries.
• Engage with a global community via posts, discussions, and collaborative projects.

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Use Cases

• Education: free, accessible courses that lower barriers to entry in deep learning.
• Research & Tools: open-source libraries designed to simplify deep learning workflows and improve reproducibility.
• Industry & Society: content exploring the ethical, governance, and societal implications of AI deployments.

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Core Features

• Free, code-first deep learning courses (Practical Deep Learning for Coders and more)
• Open-source libraries: nbdev, fasttransform, fastai, fastdownload, etc.
• Comprehensive educational content spanning ML theory, practice, and ethics
• Community-driven knowledge sharing with regular updates and new releases
• Emphasis on reproducibility and practical deployment