The Illustrated Transformer
The best visual walkthrough of attention and tensor flow before you dive into code.
This is the visual explainer that turns the Transformer from math vocabulary into a machine you can picture.
If the original paper is the blueprint, this is the animation that shows where the tensors flow.
The article turns the Transformer into a visible pipeline: text becomes vectors, vectors exchange context, and the final vector becomes a probability distribution.
The quick digest
The Illustrated Transformer walks through the same architecture with diagrams instead of dense notation. It shows tokens becoming embeddings, embeddings getting position information, and then each layer repeatedly mixing context through attention and transforming each token with a small neural network.
The most useful part is the query/key/value mental model. A token creates a query, compares it with keys from other tokens, and uses the resulting scores to blend their values. That sounds abstract, but it is just a learned “what should I pay attention to?” system.
You come away with a picture of the repeated block: attention gathers context, the feed-forward layer processes it, residuals preserve useful signals, and the decoder turns the final vector into probabilities for the next token.
What to remember
Read it like this
- First pass: Follow the pictures before the prose.
- Second pass: Pause at query/key/value and explain it aloud without equations.
- Then build taste: Then compare the diagram back to “Attention Is All You Need.”
Recreate the diagrams with toy tensors in a notebook: three tokens, tiny vectors, one attention head.