generative-ai
6 free lessons tagged generative-ai across AI, Programming. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Synthesis: Why Three Seconds Is Enough to Clone a Voice
Once audio is a sequence of tokens, generating speech becomes the same shape of problem as generating text, and the whole language modelling toolkit transfers. That reframing produced zero-shot voice cloning from about three seconds of audio. This lesson covers the codec language model approach, why so little reference suffices, the flow-matching alternative, and what the capability implies.
Generative AI for content: what it does well and badly
A practical, honest account of using generative models to make social content. Covers the jobs they genuinely do well, the specific ways they fail, why a model that sounds confident can still be wrong, the disclosure rules that now apply in the EU, and why the tool that removes effort also removes the thing that made your work worth watching.
Diffusion language models: generating text all at once
How diffusion was rebuilt for discrete text: masked diffusion instead of Gaussian noise, parallel refinement instead of token-by-token decoding, the speed economics that make it attractive, and the honest trade-offs against autoregressive transformers.
Flow matching: straightening the path from noise to data
The reframing that took over frontier image generation: learn a velocity field that transports noise to data along direct paths. Conditional flow matching, rectified flow, why straight trajectories mean fewer sampling steps, and how diffusion becomes a special case.
Diffusion models: learning to create by learning to denoise
How diffusion models generate images and more: the forward process that destroys data with noise, the reverse process that learns to undo it, the surprisingly simple training objective, and the network backbones (U-Net, DiT) that make it work.
Mastering Retrieval-Augmented Generation (RAG)
Explore Retrieval-Augmented Generation (RAG), a powerful technique that enhances Large Language Models (LLMs) by grounding their responses in external, up-to-date, and domain-specific information, mitigating hallucinations and improving factual accuracy. This lesson covers its core components, workflow, and practical considerations.