Generative Models

GANs, VAEs, and Diffusion Probabilistic Models (DDPM/DDIM).

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Compare major generative model families
Train and evaluate a small generator
Design a responsible generative pipeline

Helpful before starting

Probability distributions and likelihoods
Neural networks and backpropagation
Linear algebra and sampling

Start here

Generative Models, in plain language

GANs, VAEs, and Diffusion Probabilistic Models (DDPM/DDIM). Generative models learn data distributions for synthesis, representation learning, simulation, compression, and uncertainty-aware design.

For a small example, a toy dataset clusters near -2 and +2. Compare a single Gaussian with a mixture and inspect which can represent both modes without averaging them away. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Explicit vs implicit density models and latent variables.
Autoregressive, VAE, GAN, diffusion, and flow intuitions.
Sampling, reconstruction, likelihood, diversity, and fidelity.

Use it well

When Generative Models helps—and where it breaks

A team wants realistic tabular data for testing. Define utility and privacy goals, train on scoped data, compare marginals and relationships, and test memorization risk. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Selecting samples by eye. Better approach: Use fixed seeds, blind review, and distribution-level metrics.
Optimizing fidelity while losing diversity. Better approach: Track both precision-like and recall-like behavior.
Treating filters as a complete safety strategy. Better approach: Layer dataset, model, prompt, output, and policy controls.

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