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1. GPT

2. Your best quote that reflects your
approach… “It’s one small step for
man, one giant leap for mankind.”
- NEIL ARMSTRONG

3. GPT
(Generative Pre-trained Transformer)
Generative
•It can generate new text based on what you give it.
•Like writing emails, essays, poems, or answers.
"Generative" = it doesn’t just store info, it creates new content.
Pre-trained
•Before you ever use it, it’s already trained on huge text data (books, websites, code, etc.).
•It learned the patterns of language, so you don’t have to teach it from scratch.
"Pre-trained" = already smart when you use it!

4. Transformer
The Transformer focuses on the important words in a sentence using a method called "attention".
Transformer as a super-smart system that can:
Understand which words matter most in a sentence,
Focus on the right context, even from far away words,
Generate better sentences based on that.
EXMPLE => "The cat sat on the mat because it was tired."
what does "it" refer to?
A simple model might be confused.
A Transformer uses attention to understand:
“‘It’ refers to the cat because it was doing the action.”

5. Key Concepts in Transformer
Concept Simple Meaning
Attention Focus more on important words in the sentence
Self-Attention
The model looks at the entire sentence, including
the word itself
Layers
Repeats the attention process multiple times for
deeper understanding
Positional Encoding
Since transformers don’t use order naturally, they
add position info (word 1, word 2, etc.) so they
understand the sequence of words

6. Core Building Blocks of a
Transformer
A basic Transformer is made of two parts:
1.Encoder – Understands the input
2.Decoder – Generates the output

7. Word embedding
Word embedding is a technique used in natural language processing (NLP) to convert words into numbers (vectors) so
that a machine learning model can understand them.
Why Do We Need It?
Computers don’t understand text—they only understand numbers.
So, when you write:
"I love pizza"
The model can't understand the words "love" or "pizza" unless you first turn them into numbers.
That’s where word embeddings come in.

8. What Is a Word Embedding?
A word embedding is a vector (list of numbers) that represents a word.
Each word gets a unique vector based on its meaning and context.
"king" → [0.2, 1.4, -0.9, ...]
"queen" → [0.1, 1.3, -1.0, ...]
"apple" → [2.1, -0.3, 0.8, ...]
Words that are similar in meaning get vectors that are close together.

9. What Makes It Smart?
Unlike just assigning a number to a word (like "apple" = 5), embeddings are learned from large
amounts of text. So:
“king” and “queen” are close in meaning, so their vectors are close together.
“apple” and “banana” are both fruits, so they’re nearby.
“king” and “banana” are unrelated, so they’re far apart.
This helps models understand relationships between words.

10. Contextual Embeddings
Traditional Word Embeddings Like Word2vec Or Glove (Which Always Give The Same Vector For A
Word), Contextual Embeddings Give Different Vectors Depending On The Word's Meaning In A
Sentence.
How Do They Work?
Contextual embeddings come from Transformer models like BERT, GPT, T5, etc.
Here’s what happens:
1.Input sentence is broken into tokens.
2.The whole sentence is processed together by the Transformer.
3.Each word gets a vector that depends on its surrounding words.
4.So, every word is represented in the context of the sentence.

11. Simple Example:
Let’s take the word “bank”:
Sentence 1:
“She sat by the bank of the river.”
Sentence 2:
“He deposited money in the bank.”
🧠 In traditional embeddings (like Word2Vec):
•“bank” → [same vector] (no matter the meaning)
💡 In contextual embeddings (like BERT, GPT):
•“bank” (river) → vector A
•“bank” (money) → vector B
These vectors are
➡️ different because the model understands the context!