📐MediumEmbeddings & Vector SearchPREMIUM

Dimensionality Reduction for Embeddings

Compare PCA, t-SNE, and UMAP for visualizing and compressing embeddings, and learn when MRL and product quantization replace post-hoc reduction.

What you'll master

PCA: variance-maximizing linear projection

t-SNE: local structure preservation via KL divergence

UMAP: topological graph-based approach

Matryoshka Representation Learning (MRL) for native dimension truncation

Product quantization for billion-scale vector systems

Binary quantization for extreme compression

Impact of reduction on downstream task accuracy

Medium30 min readIncludes code examples, architecture diagrams, and expert-level follow-up questions.

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