📄️ Table of contents
Free discovery course on generative AI in 6 lessons. What generation actually means, how diffusion produces images, how text generation works, how to use these tools well, and the legal and ethical limits.
📄️ 1. What generating means
Lesson 1 of the free generative AI course: the difference between learning a boundary and learning a distribution, why generation is harder, and the succession of approaches from GANs to diffusion.
📄️ 2. How images are generated
Lesson 2 of the free generative AI course: how diffusion models learn to reverse noise, how a text prompt steers generation, what guidance and seeds control, and how ControlNet and LoRA add precision.
📄️ 3. How text is generated
Lesson 3 of the free generative AI course: why predicting the next token produces coherent writing, what temperature and top-p actually change, how the context window constrains everything, and why fabrication is structural.
📄️ 4. Using it well
Lesson 4 of the free generative AI course: the verification-cost rule for deciding what to automate, five prompting techniques that reliably work, and what to keep humans on.
📄️ 5. The limits that matter
Lesson 5 of the free generative AI course: why fabrication is structural, where the copyright position actually stands, memorisation and data leakage, deepfakes, bias, and the real cost of generation.
📄️ 6. Recap and FAQ
Lesson 6 of the free generative AI course: a synthesis of the five lessons, a practical usage guide, twelve frequently asked questions, and a vocabulary you can use precisely.
📄️ 7. Quiz and attestation
Test your understanding of generative AI with a free 5-question quiz, corrected and explained: how diffusion models work, why generation is not retrieval, hallucination, and the copyright and provenance questions.