Member-only story
Understanding and Practicing Embedding in Generative AI: From Basics to Advanced
You can also find this post on my personal blog at
https://blog.jangwook.net/10c6c81a-5407-8001-85cf-ef3c82f10e14
1. Introduction
In this post, we will delve into Embedding, a pivotal component in generative AI, exploring it from foundational concepts to advanced topics. Additionally, we will present a practical project using Python to demonstrate its real-world applications. This content is tailored for engineers and researchers interested in natural language processing (NLP) and machine learning, aiming to provide a systematic understanding of Embedding and its practical implementation.
2. Fundamental Concepts of Embedding
2.1 Definition of Embedding
Embedding is a technique that transforms data from high-dimensional to low-dimensional vector spaces. It primarily converts data such as text, images, and audio into numerical representations that machine learning models can comprehend. Embedding captures the semantic characteristics of the data, ensuring that similar data points are positioned close to each other in the vector space through the learning process.
