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.

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: the Core ML: Algorithms from Scratch course, the Deep Learning from Scratch course, Build a Transformer from Scratch — The Capstone (Transformers & LLMs from Scratch)
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, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Inference