Archived Posts

Transformers have transformed the field of sequence modeling by replacing recurrent nets with self-attention. I was curious about how this works under the hood, so I dove into the “Attention Is All You Need” paper and built the components from scratch in PyTorch. In this post I share my understanding and hands-on insights. I’ll cover the motivation behind Transformers, break down the core ideas, and show PyTorch snippets for the key modules: scaled dot-product attention, multi-head attention, positional encoding, and an encoder layer. ...