From Euclidean Distance to Spatial Classification: Unraveling the Technology behind GPT Models
In this paper, we present a comprehensive analysis of the technology underpinning Generative Pre-trained Transformer (GPT) models, with a particular emphasis on the interrelationships between Euclidean distance, spatial classification, and the functioning of GPT models. Our investigation begins with a thorough examination of Euclidean distance, elucidating its role as a fundamental metric for quantifying the proximity between points in a multi-dimensional space.