Understanding Transformers

The transformer architecture has fundamentally changed how we approach sequence modeling in machine learning. But what makes it so special? Let's build intuition from the ground up.

The Problem with Sequential Processing

Before transformers, models processed sequences one element at a time — like reading a book and forgetting the beginning by the time you reach the end.

Before transformers, models like RNNs and LSTMs processed sequences one element at a time. Imagine reading a book one word at a time, and by the time you reach the end of a sentence, you’ve already forgotten the beginning. That’s the fundamental limitation.

Enter Attention

The key insight of transformers is attention — the ability to look at all parts of the input simultaneously and decide which parts are most relevant to each other.

Think of it like this: when you read the sentence “The animal didn’t cross the street because it was too tired,” you instantly know that “it” refers to “the animal,” not “the street.” You don’t process this sequentially; you understand the relationships between all words at once.

How Attention Works

At its core, attention computes three things for each word:

• Query: What am I looking for?
• Key: What do I contain?
• Value: What do I actually represent?

The model learns to match queries with keys to determine what to pay attention to, then uses those attention scores to weight the values.

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Self-Attention: The Magic Ingredient

Self-attention allows each position in the sequence to attend to all other positions, including itself. This creates rich representations where each word is contextualized by its relationships to every other word.

# Simplified attention mechanism
def attention(Q, K, V):
    scores = Q @ K.T / sqrt(d_k)
    weights = softmax(scores)
    return weights @ V

Why Transformers Won

1. Parallelization: Unlike RNNs, transformers can process entire sequences in parallel
2. Long-range dependencies: Direct connections between any two positions
3. Scalability: Performance improves predictably with more data and compute

The Impact

From GPT to BERT to the latest multimodal models, transformers have become the foundation of modern AI. They’ve shown us that with the right architecture, models can develop surprisingly sophisticated understanding of language, code, images, and more.

The beauty is in the simplicity: attention is all you need.

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What aspects of transformers are you most curious about? The mathematics? The training process? How they generate text? Let me know what to explore next.