Understanding Attention in Transformers: A Deep Dive from First Principles

Attention — The idea that lets every token decide what matters most.

One could argue that these are all “old news.” In many ways, they are. But these are also the ideas that laid the foundation for today’s AI revolution. After years of using AI as a functional illiterate, I finally decided to organize these concepts in my head!

This is my deep dive into the core concept that started today’s AI revolution.

Not a Medium member? You can read this article for free on my blog here: TNKRD Tech Blog.

Sequence-to-Sequence (Seq2Seq)

I read several different takes on where the story of modern AI begins. For me, the most convincing starting point is the Sequence-to-Sequence model. Seq2Seq is a neural network architecture designed to transform one sequence into another. Typical applications include machine translation, text summarization, question answering, and speech recognition.

The architecture consists of two cooperating recurrent neural networks (RNNs):

• Encoder — reads and encodes the input sequence.
• Decoder — generates the output sequence.

Key Idea

The encoder compresses the entire input sequence into a single context vector.