Transformers Explained: The Architecture Behind ChatGPT

Public lesson preview

Understand the Transformer architecture that powers ChatGPT, Claude, Gemini, BERT and modern GPT models - the attention mechanism explained step by step.

Learning objectives

By the end of this lesson, you will understand:

• ✓⏱️ The Problem with RNNs: Processing Text Sequentially
• ✓👀 The Self-Attention Mechanism
• ✓🧠 Multi-Head Attention
• ✓📍 Positional Encoding
• ✓🏗️ The Full Transformer Architecture

Lesson outline

1. ⏱️ The Problem with RNNs: Processing Text Sequentially
2. 👀 The Self-Attention Mechanism
3. Query, Key, and Value — In Plain English
4. Why the Square Root Scaling?
5. 🧠 Multi-Head Attention
6. 📍 Positional Encoding
7. 🏗️ The Full Transformer Architecture
8. Encoder
9. Decoder
10. 🆚 BERT vs GPT: Two Flavours of Transformer
11. BERT (Encoder-Only)
12. GPT (Decoder-Only)
13. 📈 Why Scale Matters
14. ⚡ Training vs Inference
15. Key Takeaways