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 by step.
How multiple heads help the model grasp complex patterns more effectively.

No fluff, no shortcuts—just a clear, math-backed explanation that builds your understanding from the ground up!



Timestamps:
0:00 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



Follow my entire Transformers playlist :

Transformers Playlist: https://www.youtube.com/watch?v=lRylkiFdUdk&list=PLuhqtP7jdD8CQTxwVsuiFYGvHtFpNhlR3&index=1&t=0s



RNN Playlist: https://www.youtube.com/watch?v=lWPkNkShNbo&list=PLuhqtP7jdD8ARBnzj8SZwNFhwWT89fAFr&t=0s

CNN Playlist: https://www.youtube.com/watch?v=E5Z7FQp7AQQ&list=PLuhqtP7jdD8CD6rOWy20INGM44kULvrHu&t=0s

Complete Neural Network: https://www.youtube.com/watch?v=mlk0rddP3L4&list=PLuhqtP7jdD8CftMk831qdE8BlIteSaNzD&t=0s

Complete Logistic Regression Playlist: https://www.youtube.com/watch?v=U1omz0B9FTw&list=PLuhqtP7jdD8Chy7QIo5U0zzKP8-emLdny&t=0s

Complete Linear Regression Playlist: https://www.youtube.com/watch?v=nwD5U2WxTdk&list=PLuhqtP7jdD8AFocJuxC6_Zz0HepAWL9cF&t=0s