7 articles
Transformers see a sentence all at once, which means they are naturally blind to word order. In our final lesson of Module 5, we learn how AI adds the 'GPS of words' to stay organized.
Why does an LLM need 96 layers? In this lesson, we explore how stacking attention blocks creates a hierarchy of meaning, moving from basic letters to complex abstract logic.
Why is 'Self-Attention' the most important invention in AI history? In this lesson, we use a simple library analogy to explain how LLMs decide what to focus on.
Before-Transformer (B.T.) and After-Transformer (A.T.). In this lesson, we learn about the architectural breakthrough that allowed AI to finally understand context at scale.
From rule-based systems to GANs and the massive Transformer breakthrough.
Attention Mechanisms and Context. Understanding the 'Secret Sauce' that allows AI to reason across long documents.
The engine under the hood. A non-math guide to the Transformer architecture that powers all modern LLMs.