UMAP is a new dimensionality reduction technique that offers increased speed and better preservation of global structure.
![Understanding UMAP](https://cdn-ak-scissors.b.st-hatena.com/image/square/9afa4069c678f767abfed7ce8849cf1a18a08bf3/height=288;version=1;width=512/https%3A%2F%2Fpair-code.github.io%2Funderstanding-umap%2Fshare.png)
Part I (see Part II) of a series of expository notes accompanying this paper, by Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda Viégas, and Martin Wattenberg. These notes are designed as an expository walk through some of the main results. Please see the paper for full references and details. Language is made of discrete structures, yet neural networks operate on continuous dat
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