t-SNE / UMAP
Dimensionality reduction: project high-dimensional vectors (embeddings) down to 2D/3D for visualisation.
The standard tool for "seeing" what an ML model has learned — clusters in embedding space. t-SNE: good at local structure, slow. UMAP: faster, preserves both local and global structure. PCA is the classic linear alternative.