Learning Resources
Curated courses, tutorials, books, and videos to accelerate your AI journey.
Practical Deep Learning for Coders
Other · Jeremy Howard, Rachel Thomas
Jeremy Howard and Rachel Thomas's course at fast.ai takes a top-down, practical approach to deep learning: start with working code that does useful things, then learn the theory that explains why it works. The full course (Part 1 and Part 2) covers neural networks from basics through to diffusion models and large language models. The pedagogical approach is unusual and effective for practitioners with programming experience but limited mathematics. AICI recommends this as the most accessible rigorous introduction to deep learning for governance professionals who need genuine technical grounding.
Short Courses — DeepLearning.AI
Other · DeepLearning.AI
DeepLearning.AI's short course catalogue covers practical AI development topics in one-to-two-hour modules: prompt engineering, LangChain, building with LLM APIs, AI agents, RAG systems, and more. The courses are taught by practitioners including Andrew Ng and leading researchers from Anthropic, OpenAI, and Google. They are free to audit. AICI recommends them for technical practitioners who need to understand how AI systems are built from the inside — which is prerequisite to governing them from the outside. Understanding what a vector database is, or how RAG introduces failure modes, or what function calling means for AI agency, is increasingly necessary for serious AI governance work.