Phase 0: Attention Mechanisms · ~75 minutes · Python

The Full Transformer — Encoder + Decoder

Attention is the star. Everything else — residuals, normalization, feed-forward, cross-attention — is the scaffolding that lets you stack it deep.

Hiring signal: Understanding of the full transformer — encoder + decoder internals

What you will learn

Implement the full transformer — encoder + decoder from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 7 · 02 (Self-Attention), Phase 7 · 03 (Multi-Head Attention), Phase 7 · 04 (Positional Encoding) Time: ~75 minutes

The Problem

A single attention layer is a feature extractor, not a model. One matmul per layer is not enough capacity for language. You need depth — and depth breaks without the right plumbing.

The 2017 Vaswani paper packaged six design decisions that turned one attention layer into a stackable block. Every transformer since — encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5) — inherits the same skeleton. In 2026 the blocks have been refined (RMSNorm, SwiGLU, pre-norm, RoPE) but the skeleton is identical.

This lesson is the skeleton. Next lessons specialize it — 06 for encoders, 07 for decoders, 08 for encoder-decoder.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Transformer