EP 44 | CS224N: Transformers

Last week, we saw how RNNs struggled with the "Bottleneck Problem" and sequential processing. This week, we explore the architecture that solved it and changed natural language processing forever: the Transformer. We break down how dropping recurrence in favor of pure attention mechanisms allowed models to scale massively, process data in parallel, and understand context like never before.Key Topics:Breaking the Sequential Bottleneck: Why moving away from step-by-step processing (like RNNs) was essential for taking advantage of modern GPU hardware.Self-Attention Mechanism: How the model uses Queries, Keys, and Values to calculate the relevance of every word to every other word in a sentence simultaneously.Multi-Head Attention: Why the model looks at the exact same sentence through multiple different "lenses" at once to capture different grammatical and semantic meanings.Positional Encoding: Since Transformers process everything at once rather than left-to-right, we explain how they use clever math to inject the concept of word order back into the data.Note: This is an AI-generated discussion created using Google's NotebookLM, based on publicly available Stanford University course material (specifically CS224N) and personal study notes from my learning journey.