Lesson 34: Introduction to Transformers

What Are Transformers?

Transformers are a neural network architecture introduced in 2017 that replaced RNNs and CNNs for most sequence‑based tasks. They rely on a mechanism called self‑attention, which allows the model to understand relationships between all parts of a sequence at once.

Why Transformers Changed Everything

• They process sequences in parallel instead of step‑by‑step.
• They capture long‑range dependencies better than RNNs.
• They scale extremely well with data and compute.
• They form the foundation of modern large language models (LLMs).

The Self‑Attention Mechanism

Self‑attention computes how important each word is relative to every other word in a sentence.

Attention(Q, K, V) = softmax(QKᵀ / √d) V

This allows the model to focus on relevant context dynamically.

Key Components of Transformers

Multi‑Head Attention

Multiple attention “heads” learn different types of relationships in parallel.

Positional Encoding

Since transformers process tokens simultaneously, positional encodings provide information about order.

Feed‑Forward Networks

Each layer includes a small neural network applied to each token independently.

Layer Normalization and Residual Connections

These stabilize training and help gradients flow through deep networks.

Encoder–Decoder Architecture

The original transformer has two parts:

• Encoder — processes input sequences.
• Decoder — generates output sequences.

This structure is used in translation and sequence‑to‑sequence tasks.

Modern Variants

• BERT — encoder‑only, great for understanding text.
• GPT — decoder‑only, great for generating text.
• T5 — encoder‑decoder, unified text‑to‑text framework.
• Vision Transformers (ViT) — apply transformers to images.

Example: Using a Transformer with Hugging Face

from transformers import pipeline

classifier = pipeline("sentiment-analysis")
print(classifier("I love learning about transformers!"))

Applications of Transformers

• Language modeling and text generation
• Machine translation
• Speech recognition
• Image classification and segmentation
• Reinforcement learning
• Code generation

Why Transformers Matter

• They are the backbone of modern AI systems.
• They scale to billions of parameters.
• They outperform older architectures on nearly all benchmarks.
• They enable general‑purpose models that can perform many tasks.

Next Steps

Now that you understand transformers, you're ready to explore how large language models work in Lesson 35: Large Language Models (LLMs).

← Back to Lesson Index