Input → [Embedding] → [Self-Attention] → [Feedforward] → Output
                    ⬆️
         (Focuses on important words)

Imagine you’re reading a book 📖, but instead of reading one word at a time (like RNNs) or forgetting old sentences, you scan the entire page at once and quickly find the most important words.

That’s what Transformers do! They understand the entire input at once instead of processing it sequentially.

1. What is a Transformer?

A Transformer is a machine learning model that helps computers understand and generate text like humans.

💡 Why are Transformers powerful?

✅ They process words all at once, not one by one.

✅ They use attention mechanisms to focus on important words in a sentence.

✅ They allow models like GPT (ChatGPT) and BERT to be so smart!

2. How Does a Transformer Work?

A Transformer has two main parts:

• Encoder 📥 → Reads and understands the input sentence.
• Decoder 📤 → Generates the output sentence (translation, response, etc.).

💡 Example:

Input: "I love cats" → Encoder processes it

Output: "J'aime les chats" → Decoder generates it

Unlike Seq2Seq models, which process text step by step, Transformers process all words at the same time and use attention to focus on the most important parts.

3. What is Self-Attention? (The Secret Sauce)

🔹 Self-Attention allows Transformers to focus on key words in a sentence.

💡 Example: