Module 6 Overview

Module 6 Overview#

Theme#

Attention and transformers

Essential Question#

How does self-attention replace fixed recurrence for language and multimodal tasks?

Professional Scenario#

A platform team is evaluating whether a transformer block can support document understanding without hiding how token interactions are weighted.

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: attention mechanism analysis.

  • 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.