Attention mechanism lets ML models focus on the most relevant parts of input data, improving accuracy by assigning higher importance to crucial elements.
Human Inspiration
Inspired by how humans focus on key details and ignore distractions, attention helps models prioritize important information in complex tasks.
How Does It Work?
The model computes attention weights for each input part, highlighting what matters most for the current prediction or context.
Key Components
Attention uses queries, keys, and values. It compares queries with keys to assign weights, then combines values for the output.
Why Use Attention?
It helps models handle long or complex data, improves performance, and makes predictions more interpretable by showing what the model "looked at".
Real-World Applications
Attention powers translation, text summarization, image recognition, and large language models like ChatGPT and transformers.
Impact on AI
Attention mechanisms have revolutionized deep learning, enabling breakthroughs in NLP, vision, and generative AI by letting models focus where it matters most