Transformer Architecture

In the previous lessons, you learned the two most important building blocks of Transformers: Self-Attention and Positional Encoding.

Now it is time to put everything together.

In this lesson, you will understand the complete Transformer Architecture — how all components are arranged, why each part exists, and how information flows from input to output.

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Why Transformer Architecture Matters

Transformers are the foundation of modern NLP models such as:

• BERT
• GPT
• T5
• Modern translation and chatbot systems

Understanding the architecture means you can understand almost every modern NLP model built today.

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High-Level View of the Transformer

A Transformer consists of two main blocks:

• Encoder
• Decoder

Some models use both (translation), while others use only one (BERT → encoder only, GPT → decoder only).

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Transformer Encoder (Overview)

The encoder is responsible for understanding the input text.

It converts raw text into rich, context-aware representations.

The encoder stack is made of multiple identical layers.

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Inside One Encoder Layer

Each encoder layer contains:

1. Multi-Head Self-Attention
2. Add & Normalize
3. Feed Forward Neural Network
4. Add & Normalize

This structure is repeated many times.

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Multi-Head Self-Attention

Instead of using a single attention mechanism, Transformers use multiple attention heads.

Each head focuses on different aspects:

• One head may focus on grammar
• Another on meaning
• Another on long-distance relationships

The outputs from all heads are combined.

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Add & Normalize (Residual Connections)

After self-attention, the input is:

• Added back to the output (residual connection)
• Normalized (layer normalization)

This helps:

• Stable training
• Better gradient flow
• Faster convergence

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Feed Forward Neural Network

Each encoder layer has a small fully connected neural network.

It processes each word independently but uses the context learned from attention.

This allows the model to:

• Transform representations
• Learn complex patterns

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Transformer Decoder (Overview)

The decoder is responsible for generating output text.

It is used in tasks like:

• Translation
• Text generation
• Chatbots

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Inside One Decoder Layer

Each decoder layer contains:

1. Masked Self-Attention
2. Add & Normalize
3. Encoder–Decoder Attention
4. Add & Normalize
5. Feed Forward Network
6. Add & Normalize

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Masked Self-Attention (Why Masking?)

During text generation, the model must not see future words.

Masking ensures:

• Only past words are visible
• No information leakage

This is critical for correct generation.

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Encoder–Decoder Attention

This attention layer allows the decoder to:

• Look at the encoder output
• Focus on relevant input words

Example in translation:

The decoder aligns output words with input words.

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Stacking Multiple Layers

Transformers stack multiple encoder and decoder layers.

Each layer refines representations further.

Deeper models usually capture:

• Shallow layers → syntax
• Middle layers → semantics
• Deeper layers → task-specific meaning

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Why Transformers Are Powerful

Transformers provide:

• Parallel processing
• Strong long-range dependency handling
• Scalability to large datasets

This is why they replaced RNN-based systems.

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Transformer Architecture in One Line

A Transformer is a stack of attention-based layers that understand and generate language efficiently.

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Practice Questions

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Quick Quiz

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Homework / Assignment

Conceptual:

• Draw the Transformer architecture on paper
• Label encoder and decoder components

Preparation:

• Revise self-attention and positional encoding
• Get ready to learn BERT architecture next

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Quick Recap

• Transformers use encoder and decoder blocks
• Self-attention is the core mechanism
• Residual connections stabilize learning
• This architecture powers modern NLP

Next lesson: BERT Overview