Free guides explaining AI concepts in plain English. No jargon walls, no PhD required. Start with the basics or jump to what interests you.
New to AI? Start here. A practical roadmap for beginners.
BeginnerHow computers learn from data instead of being explicitly programmed.
BeginnerThe computing systems behind modern AI, loosely inspired by the brain.
BeginnerNeural networks with many layers — the tech behind today's breakthroughs.
BeginnerGPT-4, Claude, Gemini — how they work and why they matter.
IntermediateThe architecture behind virtually every major AI model since 2017.
IntermediateData, GPUs, and the pipeline from raw text to working AI.
IntermediateAdapting pre-trained models for specific tasks and domains.
IntermediateReinforcement Learning from Human Feedback — how ChatGPT got helpful.
PracticalGetting better results from AI models through better instructions.
IntermediateRetrieval-Augmented Generation — giving AI access to your data.
IntermediateHow AI converts text and images into numbers that capture meaning.
IntermediateAutonomous systems that plan, use tools, and take actions.
BeginnerHow AI understands and generates human language.
BeginnerHow AI processes images and video — object detection to medical imaging.
IntermediateThe technology behind Stable Diffusion, DALL-E, and Midjourney.
BeginnerModels that process text, images, audio, and video together.
IntermediateAI that learns by trial and error, rewards, and penalties.
BeginnerLlama, Mistral, and the movement to democratize AI.
ImportantKeeping AI reliable, aligned, and under human control.
ImportantBias, fairness, transparency, and the moral questions of AI.
PolicyEU AI Act, US policy, and global rules for AI.
AdvancedPrompt injection, jailbreaks, and how AI systems are defended.
IntermediateMMLU, HumanEval, and how we measure AI performance.