Understanding the Transformer Architecture
An interactive journey through the revolutionary architecture that powers modern AI
What are Transformers?
Transformers are a type of neural network architecture that has revolutionized the field of natural language processing and beyond. Unlike previous sequence models like RNNs and LSTMs, Transformers process entire sequences in parallel, making them more efficient and better at capturing long-range dependencies.
The key innovation in Transformers is the attention mechanism, which allows the model to focus on different parts of the input sequence when producing each element of the output.
Learning Journey
This interactive webpage will guide you through the Transformer architecture step by step, from the fundamental attention mechanism to the complete architecture.
- Attention Mechanism Fundamentals
- Multi-Head Attention
- Positional Encoding
- Full Transformer Architecture
Interactive Elements
Throughout this learning experience, you'll encounter various interactive elements designed to help you understand the concepts better:
- Visual demonstrations of attention mechanisms
- Interactive visualizations with adjustable parameters
- Step-by-step walkthroughs of key processes
- Real-world examples and applications
Learning Sections
Attention Mechanism
Learn about the fundamental building block of Transformers that allows models to focus on relevant parts of the input.
Multi-Head Attention
Discover how multiple attention heads work in parallel to capture different aspects of relationships in the data.
Positional Encoding
Understand how Transformers incorporate position information into their otherwise position-agnostic architecture.
Full Architecture
Explore the complete Transformer architecture, including encoder-decoder structure, feed-forward networks, and more.