In this lecture, we continue our exploration of Transformer architectures by diving deeper into the inner workings of the attention mechanism. Building on the fundamentals of self-attention, this session explains how transformers achieve powerful representation learning through multi-head attention, positional encoding, and the Add & Norm layer.
What You’ll Learn in This Lecture
Continuation of Self-Attention: Refining key concepts, attention scoring, and matrix operations.
Multi-Head Attention Mechanism: Why multiple attention heads improve feature extraction and capture diverse patterns.
Positional Encoding: How transformers incorporate order information into sequences using sinusoidal or learned embedding.
Add & Norm Layer: The role of residual connections and layer normalization in stabilizing training and improving gradient flow.
Putting It All Together: How these components interact inside an encoder block.
Ideal For:
Students, ML practitioners, and researchers seeking a deeper understanding of how transformers process complex sequential and visual data.
Tags:
Transformers, Multi-Head Attention, Positional Encoding, Add & Norm, Deep Learning, AI Lecture, NLP, Vision Transformers, Machine Learning
