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Dimensionality reduction redux: this episode covers UMAP, an unsupervised algorithm designed to make high-dimensional data easier to visualize, cluster, etc. It\u2019s similar to t-SNE but has some advantages. This episode gives a quick recap of t-SNE, especially the connection it shares with information theory, then gets into how UMAP is different (many say better).\n\nBetween the time we recorded and released this episode, an interesting argument made the rounds on the internet that UMAP\u2019s advantages largely stem from good initialization, not from advantages inherent in the algorithm. We don\u2019t cover that argument here obviously, because it wasn\u2019t out there when we were recording, but you can find a link to the paper below.\n\nRelevant links:\nhttps://pair-code.github.io/understanding-umap/\nhttps://www.biorxiv.org/content/10.1101/2019.12.19.877522v1