NLP

Understanding Attention: Implementing a Transformer from Scratch in PyTorch When Vaswani et al. published “Attention Is All You Need” in 2017, it completely shifted the landscape of Natural Language Processing. The paper introduced the Transformer architecture, which aggressively discarded recurrent connections in favor of a self-attention pipeline. In this blog post, we walk through the critical steps of rebuilding that original mathematical breakthrough from scratch in PyTorch. 1. Discarding Recurrence: The Need for Positional Encoding Because Transformers process tokens in parallel rather than sequentially, the model inherently lacks an understanding of order. How does it know that the word “dog” came before “bites”? ...