🎯 Attention Mechanism Explorer
Discover how neural networks learn to focus on what matters most
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0 / 5 completedWhat is the Attention Mechanism?
💡 The Problem with Fixed Representations
Traditional neural networks process sequences by creating a single fixed-size representation. This becomes a bottleneck for long sequences, as all information must be compressed into one vector. The attention mechanism solves this by allowing models to dynamically focus on different parts of the input when generating each output.
All information compressed → Fixed vector → Information loss for long sequences
✨ The Attention Solution
Instead of compressing everything, attention allows the model to "look back" at all input positions and assign importance weights. When processing "cat", the model can attend strongly to "fluffy" or "pet" while ignoring irrelevant words.
Query
What am I looking for? (Current focus)
Key
What do I contain? (Each input position)
Value
What information do I provide?
Attention Weight
How relevant is this? (0 to 1)
When translating "chat" to "cat", attend to the French word, not others in the sentence
Focus on key sentences and important phrases while generating summary
Look at relevant image regions when generating each word of the caption
Attend to relevant context passages when formulating the answer