# Module 3 Overview

## Theme

Optimization, loss, and regularization

## Essential Question

How do we train deep networks reliably when loss surfaces are non-convex and data are noisy?

## Professional Scenario

A modeling team has a prototype that trains inconsistently and needs a defensible training configuration before scaling experiments.

## Learning Outcomes

By the end of this module, students will be able to:

- explain the core technical idea in precise neural-network vocabulary
- connect architecture and training choices to the shape of the data and task
- run or interpret the module lab as reproducible evidence
- identify limitations, failure modes, and next steps for a defensible experiment

## Module Components

- Book prose for conceptual framing and practitioner patterns.
- Assignment notebook for the graded artifact: optimizer comparison memo using loss curves, accuracy, and regularization tradeoffs.
- Slides and narration for structured teaching.
- Instructor notes for facilitation, misconceptions, and evidence expectations.
- Lab notebook for hands-on experimentation.
- Rubric for grading technical correctness, evidence, tradeoffs, risk analysis, and communication.