What is a Transformer?
Business Problem
How can we process sequences of text to understand context and generate meaningful responses?
Traditional Approach
- Process words sequentially (RNN/LSTM)
- Limited context window
- Slow training and inference
- Difficulty with long-range dependencies
Transformer Approach
- Process all words in parallel
- Attention mechanism for context
- Fast training and inference
- Excellent at capturing relationships
Key Concepts
- Self-Attention: Allows model to focus on relevant parts of input
- Positional Encoding: Provides word order information
- Multi-Head Attention: Multiple attention patterns in parallel
- Feed-Forward Networks: Process attended features
Transformer Architecture
Click "Show Data Flow" to see how data moves through the transformer
Self-Attention Mechanism
Example Sentence
"The student opened their book"
The
student
opened
their
book
Click on any word to see attention weights
How Self-Attention Works
- Query (Q): What information am I looking for?
- Key (K): What information do I contain?
- Value (V): What information should I provide?
- Attention Score: Q × K / √d_k (scaled dot-product)
Real Example: Customer Review Analysis
Business Case: E-commerce Review Classification
Input Review: "The product quality is poor and delivery was late"
Real-world application: Automatically categorize customer feedback for support teams