Module 2: Transformer Architecture Deep Dive
Module Overview
The Transformer architecture is the foundation of all modern Large Language Models. This module provides a comprehensive technical understanding of how Transformers work, from the fundamental attention mechanism to the complete architecture.
Learning Objectives
By the end of this module, you will be able to:
- ✅ Explain the attention mechanism mathematically and intuitively
- ✅ Implement scaled dot-product attention from scratch
- ✅ Understand self-attention and multi-head attention
- ✅ Describe the role of positional encoding
- ✅ Trace data flow through a complete Transformer block
Module Contents
Chapter 1: Attention Mechanism
Deep dive into Query, Key, Value operations, scaled dot-product attention, and the intuition behind attention.
Chapter 2: Self-Attention and Multi-Head Attention
Understanding how attention is applied within a single sequence and the benefits of multiple attention heads.
Chapter 3: Positional Encoding and Embeddings
Why position matters in Transformers, sinusoidal encoding, learned embeddings, and token representations.
Key Concepts Preview
| Concept | Definition |
|---|---|
| Attention | Mechanism to weigh the relevance of different parts of input |
| Query, Key, Value | Three projections used to compute attention |
| Self-Attention | Attention where input attends to itself |
| Multi-Head | Running multiple attention operations in parallel |
| Positional Encoding | Information about token positions in sequence |
Prerequisites
This module assumes familiarity with:
- Linear algebra (matrix multiplication, dot products)
- Neural network basics (layers, activation functions)
- Python and PyTorch fundamentals
Time Estimate
- Reading: 3-4 hours
- Coding exercises: 2-3 hours
- Quiz: 30-45 minutes
- Total: 6-8 hours