What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning
Channel: CampusX
Date: 15/04/2024

Multi-head Attention enhances the expressiveness and representational capacity of Transformers by allowing the model to attend to different parts of the input data simultaneously. By utilizing multiple attention heads, the model can capture diverse patterns and relationships in the data, enabling more effective information processing and feature extraction. This mechanism enhances the model's ability to handle complex sequences and tasks in natural language processing and other domains.

Viz Tool - https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ#scrollTo=YLAhBxDSScmV

Digital Notes for Deep Learning: https://shorturl.at/NGtXg

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#Datascience #NLP #Chatgpt #CampusX #Multiheadattention

⌚Time Stamps⌚

00:00 - Intro
01:05 - Recap - Self Attention
06:33 - The problem with Self attention
11:20 - How does multi head attention work?
19:55 - How is Multi Head attention applied?
27:37 - Multi head attention visualization