How to Visualize Your Data with Dimension Reduction Techniques

This text discusses dimensionality reduction techniques used to represent high-dimensional data in lower dimensions for better visualization and understanding. Three popular methods, PCA, t-SNE, and UMAP, are compared and contrasted based on their strengths, weaknesses, assumptions, and how they shape the visualization of data. The text also demonstrates how to use these techniques with FiftyOne library and scikit-learn for image embeddings from CIFAR-10 dataset. Additionally, it explains how to run custom dimensionality reduction methods like Isomap and CompressionVAE using scikit-learn and a forked repository respectively.