# Module 7 Book Prose

## Generative models and applications

What distinguishes discriminative training from generative modeling?

A team is considering generated text, images, or synthetic records and needs to distinguish useful generation from unsupported claims. The point of this module is not to memorize an architecture name. It is to learn how a neural-network method earns its place in a workflow: what structure it assumes, what evidence shows it is behaving sensibly, and what failure modes must be addressed before anyone relies on it.

## Core Concepts

- discriminative and generative objectives
- latent-variable models and sampling
- autoencoders, diffusion, and language-model families
- likelihood, reconstruction, and perceptual evaluation
- misuse, provenance, and disclosure

Deep learning is empirical engineering built on mathematical constraints. A model is a composition of differentiable transformations, but the practical question is whether those transformations match the data, target, objective, and operating environment. Students should read every result in this module as a claim supported by evidence: tensor shapes, loss behavior, comparisons, diagnostics, and a clear statement of limits.

## Practitioner Pattern

- Start by naming what distribution the system is intended to model.
- Compare generation quality with task utility and risk, not aesthetics alone.
- Document sampling settings, provenance, and human review expectations.
- Use constrained toy generators to understand latent structure before scaling.

These patterns are deliberately conservative. In professional work, a neural network is rarely persuasive because it is novel. It becomes persuasive when the team can reproduce the experiment, explain why the design matches the problem, compare it against a meaningful alternative, and define what would invalidate the recommendation.

## Failure Modes

- Evaluating generated outputs only by subjective preference.
- Using synthetic data without checking privacy leakage or distribution distortion.
- Conflating fluent output with factual or clinically valid output.
- Omitting disclosure, watermarking, or audit trails where generated content affects users.

Failure analysis is part of the technical work, not a separate ethics appendix. A model can be mathematically valid and still be unusable if the data are mismatched, the metric hides important errors, the compute assumptions are unrealistic, or the output will be interpreted outside its intended scope.

## Study Questions

1. What problem structure does this module's method assume?
2. Which evidence from the lab would convince a skeptical reviewer that the method is behaving as intended?
3. What baseline or diagnostic would you run before increasing model complexity?
4. What limitation would you document before handing the result to a stakeholder?
5. How would your recommendation change if the data distribution, compute budget, or risk tolerance changed?