-
Dimensionality reduction for seeing: PCA, t-SNE, and UMAP compared honestly
A dataset with 64 features cannot be plotted directly on a two-dimensional screen. But a machine learning engineer can transform those 64 features into two coordinates and produce a surprisingly beaut
-
Intro to dimensionality reduction
Dimensionality reduction helps simplify complex datasets by reducing features while retaining essential information. This post introduces the fundamentals of PCA and other popular techniques like UMAP and t-SNE, explaining their mathematical foundations, real-world applications, and the latest tools driving high-performance data analysis in 2025.