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.