Introduction to Transformers (Learn GEN AI contact +91 98407 62315 .

 

Introduction to Transformers

Teacher: Alright, class! Today, we’re going to talk about one of the most powerful architectures in AI—Transformers! 🚀

So, what is a Transformer?

Let’s take a step back. Imagine you’re reading a book. Do you read each word one by one, in order and remember only the last word? No, right? You scan the whole sentence and understand the meaning based on context.

That’s what Transformers do! Unlike older models like RNNs (which process words sequentially), Transformers look at all words at once and figure out how they relate to each other.

💡 Key Idea: Transformers understand context by paying attention to the relationships between all words at the same time.

Why are Transformers better than older models?

• RNNs (Recurrent Neural Networks) → Read words one by one (slow, forgets long-term context).

• LSTMs (Long Short-Term Memory) → Better at remembering, but still limited in long sentences.

• Transformers → Look at everything at once, making them super fast and better at understanding complex meaning.

Transformers are used in models like BERT, GPT, T5, LLaMA, and they power chatbots like ChatGPT!

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2️⃣ Self-Attention Mechanism: The Secret Sauce of Transformers

Teacher: Okay, now let’s dive into what makes Transformers so powerful—the Self-Attention Mechanism. This is like the brain of Transformers!

What is Self-Attention?

Imagine this sentence:
📝 "The cat sat on the mat because it was tired."

👉 What does "it" refer to? The cat! 🐱

But how did your brain figure that out? You paid attention to the important words in the sentence!

That’s exactly what Self-Attention does:

1. It looks at all words in a sentence at the same time.

2. It decides which words are most important in understanding the meaning.

3. It assigns higher attention scores to important words.

How does Self-Attention work? (Step by Step)

1. Every word in a sentence looks at every other word.

2. It calculates how related each word is to every other word.

3. It assigns an attention score (higher means more important).

4. The model then focuses on important words while ignoring less important ones.

💡 Example:

• Sentence: "She opened the gift and was surprised to see a puppy." 🎁🐶

• The model will give higher attention to the words "gift" → "puppy" → "surprised" because they are related!

That’s how AI understands context better than older models! 🧠✨

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