Understanding Transformer Architectures

AIArchitectureNeural Networks

A breakdown of multi-head attention and the mathematical foundations of transformer-based models.

Understanding Transformer Architectures

Transformers have revolutionized natural language processing by enabling models to handle long-range dependencies efficiently.

Core Components

  • Self-Attention: The mechanism that allows the model to weigh different parts of the input relative to each other.
  • Multi-Head Attention: Running multiple attention mechanisms in parallel to capture different types of relationships.
  • Positional Encoding: Adding information about the position of each word in the sequence.

Mathematical Representation

The attention score is calculated as:

Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V

More notes to come as I progress through the research paper!