Embeddings in machine learning allow for a mathematical way to compare texts by converting them into arrays of numbers, regardless of their length. Different models produce embeddings of varying sizes, and these can represent semantic relationships in multi-dimensional spaces. This post explores how embeddings work, their applications in technical writing, and offers an example implementation with detailed steps.
Table of contents
Input and output #First, how to literally make the embeddings #Very weird multi-dimensional space #Comparing embeddings #Sort: