100 Days of Deep Learning - What is Multi-head Attention in Transformers | Multi-head Attention v Self Attention | Deep Learning

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 Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes ============================ Did you like my teaching style? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ✨ Hashtags✨ #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