[Encoder] → → → → [Context] → → → → [Decoder]
     ⬇️                              ⬆️
 Input Sentence                 Translated Output

Imagine you're translating a sentence from English to French.

For example:

👉 Input: "I love cats" 🐱

👉 Output: "J'aime les chats"

A Sequence-to-Sequence (Seq2Seq) model helps computers convert one sequence (English) into another sequence (French, or any other language).

1. What is a Seq2Seq Model?

A Seq2Seq model has two main parts:

🔹 Encoder → Understands the input sentence.

🔹 Decoder → Generates the translated output.

💡 Problem: The encoder processes the entire input and gives only one summary (a fixed vector) to the decoder. But what if the sentence is too long? The decoder may forget important details! 😞

✅ Solution: Attention Mechanism helps the decoder focus on important words at each step. 🎯

2. How Does Seq2Seq Work?

Step 1: Encoding the Input

• The encoder reads each word and creates a hidden representation (context).
• Example:
  • "I" → 🧠💾
  • "love" → 🧠💾
  • "cats" → 🧠💾
• The last hidden state is sent to the decoder.

Step 2: Decoding the Output

• The decoder generates words one by one using the encoded context.
• But without attention, the decoder only sees the last part of the input, making translations weak. 😓