What is Self Attention?

Awesome question ! 🔥 Let’s dive into Self-Attention — the secret sauce behind Transformers 🤖 — in a super simple way with examples that anyone can understand! 🧠✨📖

🤔 What is Self-Attention?

Self-Attention is a mechanism that allows a model to look at other parts of the same input when understanding a specific part.

👀 Basically:

Each word (or patch, in images) decides how much to pay attention to every other word in the same input — including itself!

📖 Think of it like this:
When understanding the meaning of the word “bank“, the model looks at surrounding words to decide:

• Is it 🏦 (money) or 🏞️ (riverbank)?

🧠 Simple Example:

Input sentence:

“The cat sat on the mat.”

Let’s focus on “cat” 🐱

With self-attention, the model asks:

• “What do I need to know about the other words in the sentence to better understand ‘cat’?”

It might give attention scores like:

So, “cat” mostly pays attention to itself and “sat” (because they’re closely related). This helps the model understand relationships better 🧩

🔍 Why Is It Called Self-Attention?

Because the model is attending to itself — each word (or input token) looks at all other tokens in the same sequence, including itself 🔁

It’s like each word is thinking:

“Hey, what do the rest of us mean together?” 🧠💭

🧪 Where Is Self-Attention Used?

✅ Transformers (BERT, GPT, etc.) 🤖
✅ Vision Transformers (ViT) 🖼️
✅ Text translation 🌍
✅ Chatbots & summarization ✍️

⚙️ How It Works (Quick Look)

Each word is turned into three vectors:

• Query (Q) ❓
• Key (K) 🗝️
• Value (V) 📦

The model computes attention like this:

Attention(Q, K, V) = softmax(Q × Kᵀ / √d) × V

📊 This math helps decide how much focus (weight) to give each word in the sentence.

🟰 Summary Table

✅ TL;DR:

Self-Attention lets each word or token in a sequence pay attention to all others — to understand context, relationships, and meaning better. 🧠✨