Transformer Architecture Components

The core components of a deep-learning transformer architecture are:

1. Self-Attention Layer

• What: Compares each word to every other word
• Why: Understands relationships and context
• Example: Links pronouns to their referents

2. Multi-Head Attention

• What: Multiple attention mechanisms in parallel
• Why: Captures different types of relationships
• Example: One head for syntax, another for semantics

3. Feed-Forward Networks

• What: Standard neural network layers
• Why: Process attended information
• Position: After attention layers

4. Positional Encoding

• What: Adds position information to words
• Why: Transformers don’t inherently know word order
• How: Mathematical encoding of position

5. Layer Normalization

• What: Normalizes activations
• Why: Stabilizes training
• Where: Throughout the network

6. Residual Connections

• What: Skip connections between layers
• Why: Helps gradient flow, enables deep networks
• Benefit: Can train very deep models (100+ layers)