Building Attention Mechanisms from Scratch
Building Attention Mechanisms from Scratch: A Deep Dive Introduction: Why Attention Changed Everything Before 2014, sequence-to-sequence models compressed entire input sequences into a single fixed-size vector—a bottleneck that lost critical information. Imagine summarizing a entire book into one sentence, then trying to translate it. The attention mechanism solved this by allowing models to dynamically focus on relevant parts of the input, revolutionizing NLP and beyond. Why and How Self-Attention Changed Everything The Fundamental Breakthrough: "Every Token Can Directly Talk to Every Other Token" Before Self-Attention (The Sequential Prison) Token1 → Token2 → Token3 → Token4 → Token5 ↑ Must pass through intermediate states Information bottleneck : Later tokens only know about earlier ones through a compressed hidden state Gradient degradation : Learning long-range dependencies requires gradients to flow through many steps Sequential computation : Can't process...