Transformer Architecture: Attention Mechanisms and Design
Contents
Overview
Transformer models are foundational architectures in modern deep learning. This post explores their design, key concepts, and applications.
Key Topics
Self-Attention
The mechanism allowing models to focus on relevant parts of input sequences.
Multi-Head Attention
Running multiple attention operations in parallel to capture different representation subspaces.
Positional Encoding
Adding position information to token embeddings to help the model understand sequence order.
Advantages over RNNs
- Parallelizability - All positions can be computed in parallel vs. sequential RNNs
- Long-range dependencies - Direct connections between all positions
- Training efficiency - Better scaling with sequence length
Implementation
Building transformers from scratch and key implementation details.
More detailed content coming soon…