Attention and Transformers - The Architecture That Changed Everything

Public lesson preview

A deep dive into self-attention, multi-head attention, positional encoding, and the Transformer architecture that powers every modern large language model.

Learning objectives

By the end of this lesson, you will understand:

• ✓Why RNNs Hit a Wall
• ✓The Attention Intuition
• ✓Query, Key, Value - The Core Mechanism
• ✓Multi-Head Attention
• ✓Positional Encoding

Lesson outline

1. Why RNNs Hit a Wall
2. The Attention Intuition
3. Query, Key, Value - The Core Mechanism
4. Multi-Head Attention
5. Positional Encoding
6. The Full Transformer Architecture
7. Why Transformers Parallelise
8. Decoder-Only vs Encoder-Only vs Encoder-Decoder
9. 📚 Further Reading