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.
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: Self-Attention from Scratch, Multi-Head Attention, Positional Encoding — Sinusoidal, RoPE, ALiBi
- 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, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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