Module 2Transformer Deep Dive

Scaled Dot-Product Attention

Implement the core attention primitive from first principles.

Why this module matters

Everything in transformers sits on top of this operation.

Prerequisites

  • ▸ Linear algebra basics

Learning objectives

  • ▸ Interpret Q, K, and V operationally
  • ▸ Explain scaling and masking
  • ▸ Implement attention in plain PyTorch

Core concepts

Queries, keys, values
Masking
Softmax weighting

Hands-on practice

  • ▸ Write a tested single-head attention function

Expected output

A reusable attention primitive with shape assertions.

Study checklist

  • ✅ Interpret Q, K, and V operationally
  • ✅ Explain scaling and masking
  • ✅ Implement attention in plain PyTorch

Common mistakes

  • ⚠️ Forgetting scale by sqrt(d)
  • ⚠️ Applying mask after softmax
  • ⚠️ Losing track of dimensions

Module rhythm

  • 1. Read the summary and why-it-matters section first.
  • 2. Work through concepts before rushing into practice.
  • 3. Use the checklist to verify real understanding, not just completion.

How to continue

Then expand one head into many and understand why that helps.

Back to course overview →

How to use this page well

Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.