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From Regression to Language: Exploring How Machines Learn Language
An ML Exploration
Preface
My initial understanding of machine learning revolved around the optimization of coefficients to map data relationships — a mathematical endeavor at its core. However, delving into language models made me realize the immense power that words hold. I began to see words not merely as data points but as repositories of meaning, shaped by millennia of human thought, emotion, and cultural evolution.
This realization illuminated an extraordinary truth: words are not bound by the minds that create them. Collections of words — books, poems, and stories — possess a life of their own, their essence persisting long after their authors fade from memory. Through their structure and interplay, they encode layers of meaning that reflect the complexity of human communication and thought.
It is this unique attribute of language — the power to transcend individuality and resonate across time — that makes models like LLaMA so remarkable. These models don’t just process words; they leverage transformer architectures and attention mechanisms to capture intricate patterns and relationships, drawing on language’s inherent nuances. By encoding these subtleties within neural network layers, they…
