An Introduction to Attention Mechanisms in Transformers
🔍 An Introduction to Attention Mechanisms in Transformers 📌 What Is Attention? Attention is a technique that allows models to focus on relevant parts of the input when making decisions — much like how humans focus their attention on certain words when reading a sentence. In the context of natural language processing (NLP), attention helps models decide which words matter most when processing or generating a sentence. 🧠 Why Is Attention Important? Before transformers, models like RNNs and LSTMs struggled with long-range dependencies — remembering important words that occurred far back in a sentence. Attention mechanisms solve this by letting the model "look at" all words in the input sequence simultaneously, assigning different weights to each word based on its relevance. ⚙️ Attention in Transformers Introduced in the landmark paper “Attention Is All You Need” (2017) by Vaswani et al., the Transformer architecture is based entirely on attention mechanisms — no recurrence, ...