Master Multi-headed attention in Transformers | Part 6
Unlock the power of multi-headed attention in Transformers with this in-depth and intuitive explanation! In this video, I break down the concept of multi-headed attention in Transformers using a relatable analogy - Just as multiple RAM modules handle different data simultaneously for better performance, Multi-headed attention processes diverse patterns in parallel to improve understanding of language. We answer the fundamental question, why just 1 head of Self-attention is not enough?
What you'll learn:
✅ Why multi-headed attention is essential for modern machine learning.✅ How it works step …
Watch on YouTube ↗
(saves to browser)
Chapters (9)
Intro
0:41
Self-attention overview
2:13
Why one head is not enough?
4:52
Analogy of RAM
6:37
Analogy of Convolutional Neural Networks
7:47
Working of Multi-head Attention
12:09
Why need Linear Transformation?
14:33
How many number of Heads to use?
16:42
Outro
DeepCamp AI