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.

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⌚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