Self Attention in Transformers Deep Learning Simple Explanation with Code

YOUR LINK HERE:

http://youtube.com/watch?v=-tCKPl_8Xb8

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. • Digital Notes for Deep Learning: https://shorturl.at/NGtXg • ============================ • Do you want to learn from me? • Check my affordable mentorship program at : https://learnwith.campusx.in/s/store • ============================ • 📱 Grow with us: • CampusX' LinkedIn: / campusx-official • CampusX on Instagram for daily tips: / campusx.official • My LinkedIn: / nitish-singh-03412789 • Discord: / discord • 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

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