Attention Mechanisms & Transformers

Learn how attention mechanisms revolutionized deep learning. Understand self-attention, transformers, and multi-head attention.

📄️ Self-Attention

Understanding how models weigh the importance of different parts of an input sequence using Queries, Keys, and Values.

A comprehensive deep dive into the Transformer architecture, including Encoder-Decoder stacks and Positional Encoding.

Understanding how multiple attention 'heads' allow Transformers to capture diverse linguistic and spatial relationships simultaneously.