100 Days of Deep Learning - Self Attention in Transformers | Deep Learning | Simple Explanation with Code!

Self Attention works by computing attention scores for each word in a sequence based on its relationship with every other word. These scores determine how much focus each word receives during processing, allowing the model to prioritize relevant information and capture complex dependencies across the sequence. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 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✨ #SelfAttention #DeepLearning #CampusX #Transformers #NLP #GENAI ⌚Time Stamps⌚ 00:00 - Intro 02:37 - Revision [What is Self Attention] 07:00 - How does Self Attention work? 24:45 - Parallel Operations 29:40 - No Learning Parameters Involved 39:10 - Progress Summarization 50:15 - Query, Key & Value Vectors 52:28 - A Relatable Example 01:07:52 - How to build vectors based on Embedding vector 01:20:08 - Summarized Matrix Attention 01:22:45 - Outro