🧠 What is an Attention Layer?
An Attention Layer is a part of a machine learning model (especially in NLP – Natural Language Processing 🗣️) that helps the model focus on the most important words when trying to understand a sentence.
🎯 Why is it called “Attention”?
Just like humans pay attention to certain words in a sentence to understand its meaning, the model does too!
🧍➡️ Imagine you’re reading:
“The cat that was sitting on the mat jumped when it saw a dog.”
To understand what happened, the word “jumped” is important. The Attention Layer helps the model give more weight (importance) to that word when making predictions.
🛠️ How does it work?
Let’s say the model is trying to translate a sentence or answer a question. The Attention Layer:
- 🔍 Looks at all the words in the input sentence.
- 📌 Figures out which words are important for the current task.
- 🧲 Focuses more on those important words (by giving them higher “attention scores”).
💡 Real-Life Example:
If you ask:
“Who is the president of the United States?”
The attention layer helps the model focus on:
- “president” 👔
- “United States” 🇺🇸
And less on words like “who” or “is”.
🔁 Used in:
- Transformers (like GPT, BERT) 🤖
- Chatbots 💬
- Translation apps 🌍
- Speech recognition 🎙️
🧩 Simple Summary:
Attention Layer = Smart highlighter 🖍️
It helps the model pay attention to the most useful words so it can understand or respond better!
