ADVANCED NLP

Transformers are one of the most important inventions in modern deep learning. They help computers understand language, generate text, translate between languages, analyze images, and even create art.

Transformers replaced older models like RNNs because they learn faster, understand long sentences better, and work extremely well on large datasets.

In this chapter, we will learn about the self-attention mechanism, the encoder–decoder architecture, and famous Transformer models like BERT, GPT, and Vision Transformers (ViT).

1. Self-Attention Mechanism

The self-attention mechanism is the heart of a Transformer.

Simple Explanation

Self-attention helps a model decide which words in a sentence are important to each other.

Example

Sentence: "The cat sat on the mat because it was warm."

To understand "it," the model must look at "mat."

Self-attention helps it do this automatically.

Why Self-Attention Is Powerful

Understands relationships between words, no matter how far apart
Learns context better than RNNs
Works well for long sentences
Fast to train on GPUs

Intuition

Self-attention is like a student reading a paragraph and highlighting important words.

The Transformer highlights what matters for every word.

2. Encoder–Decoder Architecture

Transformers often use two main parts:

A. Encoder

Reads the input sentence
Understands meaning and context
Converts text into hidden representations

B. Decoder

Takes encoder's output
Generates new text (translation, summary, answer, etc.)

Real Example

Input: "Hello, how are you?"

Output (translated to Urdu): "السلام علیکم، آپ کیسے ہیں؟"

The encoder understands the English sentence.

The decoder generates the Urdu translation.

3. BERT (Bidirectional Encoder Representations from Transformers)

BERT uses only the encoder part of a Transformer.

It reads sentences in both directions (left and right), making it extremely good at understanding meaning.

What BERT Is Good At

Sentiment analysis
Answering questions
Classifying emails or messages
Extracting important information
Named Entity Recognition (NER)

Example

Input: "This movie was amazing!"

Output: Positive sentiment (confidence 98%)

Code Example (HuggingFace Transformers)

from transformers import pipeline

classifier = pipeline("sentiment-analysis")
result = classifier("I love deep learning!")
print(result)

4. GPT (Generative Pretrained Transformer)

GPT uses only the decoder part of the Transformer.

It is designed for text generation.

What GPT Can Do

Write essays
Generate stories
Create Python code
Answer questions
Chat like a human

Why GPT Is Special

GPT models can predict the next word in a sentence with strong accuracy.

This helps them write full paragraphs and complete tasks naturally.

Simple Example

Input: "Write a sentence about AI."

Output: "AI helps computers learn and make smart decisions."

Code Example

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")
print(generator("Deep learning is fun because", max_length=30))

5. Vision Transformers (ViT)

Transformers were originally designed for language, but researchers discovered they work amazingly well for images too.

How Vision Transformers Work

Split an image into small patches
Treat each patch like a word
Use self-attention to learn relationships between patches
Understand the entire image

Why ViTs Are Important

High accuracy for image classification
Compete with CNNs
Can learn from large image datasets
Used in medical imaging, traffic cameras, satellites, etc.

Example

A Vision Transformer can look at a picture and identify:

Cat
Car
Flower
Traffic sign

Code Example

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import requests
import torch

image = Image.open("sample.jpg")

processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)

print(outputs.logits.argmax())

6. Why Transformers Changed Deep Learning

Transformers became popular because:

They handle long sequences well
They train faster than RNNs
They work for language, images, audio, and even video
They scale extremely well with more data

Transformers power modern AI systems such as:

ChatGPT
Google Search
YouTube recommendations
Translation apps
Medical image analysis

7. Summary Table

Concept	Meaning	Example
Self-attention	Finds important relationships	"it" → "mat"
Encoder	Reads and understands input	Understanding English
Decoder	Generates output	Writing Urdu
BERT	Understanding text	Sentiment analysis
GPT	Generating text	Chatbots
ViT	Transformers for images	Image classification

ADVANCED NLP

Transformers replaced older models like RNNs because they learn faster, understand long sentences better, and work extremely well on large datasets.

In this chapter, we will learn about the self-attention mechanism, the encoder–decoder architecture, and famous Transformer models like BERT, GPT, and Vision Transformers (ViT).

1. Self-Attention Mechanism

The self-attention mechanism is the heart of a Transformer.

Simple Explanation

Self-attention helps a model decide which words in a sentence are important to each other.

Example

Sentence: "The cat sat on the mat because it was warm."

To understand "it," the model must look at "mat."

Self-attention helps it do this automatically.

Why Self-Attention Is Powerful

Understands relationships between words, no matter how far apart
Learns context better than RNNs
Works well for long sentences
Fast to train on GPUs

Intuition

Self-attention is like a student reading a paragraph and highlighting important words.

The Transformer highlights what matters for every word.

2. Encoder–Decoder Architecture

Transformers often use two main parts:

A. Encoder

Reads the input sentence
Understands meaning and context
Converts text into hidden representations

B. Decoder

Takes encoder's output
Generates new text (translation, summary, answer, etc.)

Real Example

Input: "Hello, how are you?"

Output (translated to Urdu): "السلام علیکم، آپ کیسے ہیں؟"

The encoder understands the English sentence.

The decoder generates the Urdu translation.

3. BERT (Bidirectional Encoder Representations from Transformers)

BERT uses only the encoder part of a Transformer.

It reads sentences in both directions (left and right), making it extremely good at understanding meaning.

What BERT Is Good At

Sentiment analysis
Answering questions
Classifying emails or messages
Extracting important information
Named Entity Recognition (NER)

Example

Input: "This movie was amazing!"

Output: Positive sentiment (confidence 98%)

Code Example (HuggingFace Transformers)

from transformers import pipeline

classifier = pipeline("sentiment-analysis")
result = classifier("I love deep learning!")
print(result)

4. GPT (Generative Pretrained Transformer)

GPT uses only the decoder part of the Transformer.

It is designed for text generation.

What GPT Can Do

Write essays
Generate stories
Create Python code
Answer questions
Chat like a human

Why GPT Is Special

GPT models can predict the next word in a sentence with strong accuracy.

This helps them write full paragraphs and complete tasks naturally.

Simple Example

Input: "Write a sentence about AI."

Output: "AI helps computers learn and make smart decisions."

Code Example

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")
print(generator("Deep learning is fun because", max_length=30))

5. Vision Transformers (ViT)

Transformers were originally designed for language, but researchers discovered they work amazingly well for images too.

How Vision Transformers Work

Split an image into small patches
Treat each patch like a word
Use self-attention to learn relationships between patches
Understand the entire image

Why ViTs Are Important

High accuracy for image classification
Compete with CNNs
Can learn from large image datasets
Used in medical imaging, traffic cameras, satellites, etc.

Example

A Vision Transformer can look at a picture and identify:

Cat
Car
Flower
Traffic sign

Code Example

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import requests
import torch

image = Image.open("sample.jpg")

processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)

print(outputs.logits.argmax())

6. Why Transformers Changed Deep Learning

Transformers became popular because:

They handle long sequences well
They train faster than RNNs
They work for language, images, audio, and even video
They scale extremely well with more data

Transformers power modern AI systems such as:

ChatGPT
Google Search
YouTube recommendations
Translation apps
Medical image analysis

7. Summary Table

Concept	Meaning	Example
Self-attention	Finds important relationships	"it" → "mat"
Encoder	Reads and understands input	Understanding English
Decoder	Generates output	Writing Urdu
BERT	Understanding text	Sentiment analysis
GPT	Generating text	Chatbots
ViT	Transformers for images	Image classification

deep-learning Topics

deep-learning Tutorial

ADVANCED NLP

1. Self-Attention Mechanism

Simple Explanation

Example

Why Self-Attention Is Powerful

Intuition

2. Encoder–Decoder Architecture

A. Encoder

B. Decoder

Real Example

3. BERT (Bidirectional Encoder Representations from Transformers)

What BERT Is Good At

Example

Code Example (HuggingFace Transformers)

4. GPT (Generative Pretrained Transformer)

What GPT Can Do

Why GPT Is Special

Simple Example

Code Example

5. Vision Transformers (ViT)

How Vision Transformers Work

Why ViTs Are Important

Example

Code Example

6. Why Transformers Changed Deep Learning

Transformers power modern AI systems such as:

7. Summary Table

deep-learning Topics

deep-learning Tutorial

ADVANCED NLP

1. Self-Attention Mechanism

Simple Explanation

Example

Why Self-Attention Is Powerful

Intuition

2. Encoder–Decoder Architecture

A. Encoder

B. Decoder

Real Example

3. BERT (Bidirectional Encoder Representations from Transformers)

What BERT Is Good At

Example

Code Example (HuggingFace Transformers)

4. GPT (Generative Pretrained Transformer)

What GPT Can Do

Why GPT Is Special

Simple Example

Code Example

5. Vision Transformers (ViT)

How Vision Transformers Work

Why ViTs Are Important

Example

Code Example

6. Why Transformers Changed Deep Learning

Transformers power modern AI systems such as:

7. Summary Table