Phase 0: Generative AI Foundations · ~45 minutes · Python
Generative Models — Taxonomy & History
Every image model, text model, video model, and 3D model fits in one of five buckets. Pick the wrong bucket and you will fight the math for weeks. Pick the right one and the field's last twelve years of progress stacks cleanly in your head.
Hiring signal: Understanding of generative models — taxonomy & history internals
What you will learn
- Implement generative models — taxonomy & history from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
Type: Learn Languages: Python Prerequisites: Phase 2 (ML Fundamentals), Phase 3 (Deep Learning Core), Phase 7 · 14 (Transformers) Time: ~45 minutes
The Problem
A generative model does one job: given training samples drawn from some unknown distribution p_data(x), output new samples that look like they came from the same distribution. Faces, sentences, MIDI files, protein structures — all the same problem if you squint.
The rub is that p_data lives in a space with millions of dimensions (a 512x512 RGB image is ~786k dimensions), the samples sit on a thin manifold inside that space, and you only have maybe 10M examples. Brute-forcing the density is hopeless. Every generative model is a compromise that trades one hard problem for a slightly less hard one.
Five families have survived the last twelve years. Knowing which compromise each family makes tells you why it wins on some tasks and collapses on others.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Concept, A brief history, The five-question triage, Build It, Use It, Ship It, Exercises, Key Terms, Production note: five families, five inference shapes, Further Reading — plus a hands-on lab, quiz, and project artifact.
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