preface
When I first started using transformers in 2019, I was immediately hooked. Two years later, I built my own deep learning architecture using attention. That work was later published in a Springer Nature journal, and the experience convinced me that transformers would be transformative, literally speaking. What struck me most was not their complexity but their simplicity. The mechanism that unlocked the transformer revolution is not complex mathematics. It’s built on linear algebra fundamentals: multiplying matrices, normalizing with softmax, and combining vectors with weighted sums.
It’s remarkable that from a foundation of dot products and probabilities we arrived at systems with billions of parameters that can reason across text, images, audio, and video. That’s the story of transformers: one elegant mechanism, applied at scale, reshaping the landscape of AI. This book focuses on that story—from the origins of transformers to how we can now use large language models (LLMs) and multimodal systems in practice.
The elegance lies in how those simple steps are arranged and combined. Each token is projected into queries, keys, and values. The model computes dot products between queries and keys to decide relevance, applies softmax to turn those scores into probabilities, and uses them to form weighted sums over the values.