Transformer Architecture

Introduction

The Transformer (Vaswani et al., 2017) is the neural-network architecture behind every modern LLM. Its key insight: replace slow recurrent networks with fast, parallel self-attention that lets every token 'see' every other token.

Beginner analogy: An old RNN reads a sentence word-by-word, like reading aloud. A transformer reads the whole sentence at once and instantly cross-references every word against every other — like skimming a page.

Understanding the topic

Core concepts to understand:

• Self-attention — each token weighs its relevance to every other token.
• Multi-head attention — multiple attention 'lenses' in parallel.
• Feed-forward layers — process the attention output further.
• Residual connections + LayerNorm — keep gradients flowing in deep stacks.
• Positional encoding — gives the model word-order awareness.

Syntax reference

Visual workflow / architecture:

         ┌─────── Transformer Block ───────┐
Input ──>│  Multi-head Self-Attention      │
         │             ↓                   │
         │     Add & LayerNorm             │
         │             ↓                   │
         │  Feed-Forward Network           │
         │             ↓                   │
         │     Add & LayerNorm             │──> next block
         └─────────────────────────────────┘
   Stack 12–96 of these → GPT-style LLM

Real-world use

Every modern LLM (GPT, Claude, Gemini, Llama, Mistral) is a stack of transformer blocks. The original 'Attention Is All You Need' paper has 100k+ citations — the most influential ML paper of the decade.

Best practices

• You don't need to implement transformers — but understand attention.
• Watch Andrej Karpathy's Let's build GPT video for an unforgettable explanation.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is the key innovation of the transformer?
• Q2. What is self-attention?
• Q3. Why do we need positional encoding?
• Q4. What does multi-head attention add?