# Module 2 Overview

## Theme

Backpropagation and automatic differentiation

## Essential Question

How does the chain rule enable efficient learning in deep networks?

## Professional Scenario

A research engineer needs to explain why a custom loss is not training and whether the issue is math, implementation, or scale.

## 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: gradient trace and autograd check.
- 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.