Lecture 15 - Transformers in Deep Learning: Architecture, Self-Attention, Multi-Head Attention & Positional Encoding

Introduction

Transformers are the most powerful and game-changing architecture in modern deep learning. From BERT and GPT-4 to Stable Diffusion and Gemini nearly every cutting-edge AI model today is built using the Transformer architecture. Before Transformers, RNNs, LSTMs, and GRUs dominated sequence modeling. But these older architectures suffered from sequential processing, long-range dependency issues, and slow training times.

Transformers solved all of this using self-attention, parallel processing, and multi-head attention, leading to massive improvements in accuracy, scalability, and speed. In this lecture, we explore the entire architecture of Transformers from QKV vectors to positional encoding, encoder–decoder design, and real-world applications.

This is one of the most important lectures in your Deep Learning series.

Why Transformers Were Needed

Before Transformers, deep learning models used RNN-based sequence processing:

They processed tokens one at a time
Unable to parallelize computations
Suffered from vanishing gradients
Struggled with long sentences
Lost context across distant tokens

Even attention added to RNNs improved things, but still — sequential processing limited performance.

Transformers changed everything by:

Removing RNNs entirely
Introducing Self-Attention
Allowing complete parallelism
Handling long sequences smoothly
Scaling up to billions of parameters

This shift enabled modern AI as we know it.

What Are Transformers?

A Transformer is a deep learning architecture based entirely on Attention Mechanisms.
It processes sequences in parallel instead of step-by-step.

A Transformer consists of:

Encoder (for understanding input)
Decoder (for generating output)
Self-Attention layers
Multi-Head Attention
Feed-Forward Networks
Positional Encoding

Its strength comes from its ability to:

Compare every token with every other token
Learn relationships globally
Focus on relevant words automatically
Scale on GPUs with parallel training

Transformers can learn extremely complex patterns, making them ideal for:

NLP
Vision
Audio
Multimodal AI
Generative models

Core Components of the Transformer Architecture

Let’s break down each major building block.

Input Embeddings

Every token is turned into a dense vector.
For example:

“Transformers are powerful.”

Each word is mapped to a vector like:
[0.12, 0.54, -0.33, ...]

These embeddings capture:

Semantic meaning
Contextual relationships
Word similarity

Positional Encoding

Transformers do not have any sense of order (unlike RNNs).

To teach them order, we add positional encodings.

These are sinusoidal or learned vectors added to the embeddings:

Embedding + Positional Encoding

This enables the model to know:

First token
Next token
Distances between tokens
Relative positions

This is crucial for translation, summarization, and reasoning.

Self-Attention Mechanism (QKV)

This is the heart of the Transformer.

Each token generates three vectors:

Q → Query
K → Key
V → Value

These determine:

What the token is looking for (Q)
What information it contains (K)
What information to pass (V)

Self-Attention computes:

Attention = softmax(QKᵀ / √d_k) V

This allows tokens to attend to each other.

Example:
Sentence → “The cat sat on the mat.”

The word “cat” will attend strongly to:

“The”
“sat”
“mat”

This produces context-aware embeddings.

Multi-Head Attention

Instead of using one attention pattern, Transformers use multiple:

Head 1 → learns syntax
Head 2 → learns semantics
Head 3 → learns entity relationships
Head 4 → learns long-range dependencies

Multiple heads allow the model to understand language from different perspectives simultaneously.

Feed-Forward Neural Network (FFN)

After attention, the model applies a two-layer feed-forward network.

This improves:

Representation learning
Abstraction
Feature transformation

FFN is applied independently to each position.

Residual Connections + Layer Normalization

Transformers train deep models easily because they use:

Skip connections
Layer normalization

These help gradients flow smoothly.

Lecture 14 – Attention Mechanisms in Deep Learning: Architecture, Types, Scoring Functions & Applications

Transformer Encoder Architecture

Each Encoder block contains:

Self-Attention layer
Add & Norm
Feed-Forward Network
Add & Norm

Encoders analyze input sequence holistically.

Applications:

BERT
Sentence Embeddings
Feature extraction

Transformer Decoder Architecture

The decoder adds two more components:

Masked Self-Attention
Encoder–Decoder Attention

Masked self-attention prevents the model from seeing future tokens during generation.

Encoder–decoder attention helps it focus on relevant parts of the input.

Applications:

GPT
Translation
Summarization
Text generation

Attention is All You Need Original Transformer Paper

https://arxiv.org/abs/1706.03762

How Transformers Process a Sequence (Step-by-Step)

Let’s see the flow:

Step 1 – Convert input tokens to embeddings

Words → vectors

Step 2 – Add positional encoding

Gives the model position awareness.

Step 3 – Pass through Encoder Self-Attention

Each token attends to all others.

Step 4 – Feed-Forward Network

Enhances representation.

Step 5 – Decoder Self-Attention

Decoder attends to previous generated tokens.

Step 6 – Encoder–Decoder Attention

Decoder focuses on relevant words from the Encoder.

Step 7 – Output Layer

Predicts next token.

This continues until the model generates an end token.

Advantages of Transformers

Transformers outperform previous models because they:

Train in parallel

Huge speed improvement.

Capture global relationships

Better long-range understanding.

Scale to large datasets

Billions of parameters are possible.

Show state-of-the-art performance

Every major AI task now uses Transformers.

Enable multimodal AI

Text, image, video, audio all unified.

Applications of Transformers

Transformers power:

NLP

BERT
GPT-3, GPT-4
T5
LLaMA
Mistral

Vision

Vision Transformers
Image Captioning
Diffusion Models

Speech

Whisper
Wav2Vec

Generative AI

Chatbots
Text-to-image models
Video Generation models

Industry

Medical diagnosis
Autonomous driving
Finance modeling
Robotics

Modern Variants of Transformers

BERT

Bidirectional encoder model.

GPT

Decoder-only autoregressive model.

ViT

Vision Transformer for images.

T5

Unified text-to-text transformer.

LLaMA & Mistral

Lightweight, fast, powerful models.

Why Transformers Changed AI Forever

Transformers enabled:

Larger models
Faster training
Better reasoning
Cross-modal understanding
Natural language fluency
Contextual memory over long text

They are the foundation behind every major breakthrough from 2017 to now.