In this article, you will learn what latent spaces are and how they serve three distinct roles — descriptive, generative, and predictive — across a wide range of machine learning applications.
Topics we will cover include:
How latent spaces compress high-dimensional data into structured numerical representations using techniques like Principal Component Analysis.
How the generative role of latent spaces enables the creation of entirely new data points through interpolation.
How the predictive role of latent spaces powers similarity-based applications such as recommender systems and RAG pipelines.
