Transformer Architecture

Last modified on September 15, 2025 • 1 min read • 101 words

Understanding the transformer model and attention mechanisms

Transformer Architecture  

Introduction  

Transformers have revolutionized natural language processing and are now being applied to various domains including computer vision.

Key Components  

Self-Attention Mechanism  

The core innovation that allows models to process sequences in parallel.

Multi-Head Attention  

Multiple attention heads capturing different types of relationships.

Position Encoding  

Adding positional information to input embeddings.

Feed-Forward Networks  

Point-wise fully connected layers.

Variants and Applications  

BERT  

Bidirectional encoder representations for language understanding.

GPT  

Generative pre-trained transformers for text generation.

Vision Transformer (ViT)  

Applying transformers to image classification.

DALL-E  

Text-to-image generation using transformers.