Attention in LLMs Explained (Step by Step) | Chapter 2

Описание: n this Chapter 2 of our LLM & Transformers series, we break down the Attention Layer step by step — from the original “Attention is All You Need” paper to how GPT-3 uses multi-head attention across 96 layers.

Whether you’re a student, researcher, or AI enthusiast, this video gives you both the math and the intuition behind Attention in Large Language Models (LLMs).

What you’ll learn in this video:
1. The core idea of the Attention mechanism
2. How Query, Key, and Value matrices work
3. Single-head vs Multi-head Attention explained
4. Why context size matters in GPT models
5. How embeddings are updated in Transformers
6. Real-world references from GPT-3

Chapters and timestamp:
00:00 – Welcome
01:17 – Attention Is All You Need (original paper)
01:35 – Recap
02:10 – Core idea of the Attention Layer
03:49 – Single-head Attention
06:06 – Steps of Single-head Attention
07:43 – Query matrix & Query vector
11:19 – Key matrix & Key vector
14:10 – Value matrix & Value vector
23:33 – Context size
24:50 – Updating the embeddings
29:29 – Result of updating embeddings
31:30 – GPT-3 dimensions
33:06 – Multi-head Attention
37:44 – Output matrix
42:11 – GPT-3: 96 layers reference
44:06 – Parameter count & conclusion


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