UMAP Uniform Manifold Approximation and Projection for Dimension Reduction | SciPy 2018 |
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Summary
In this video, I will give you an High-dimensional data is everywhere — 784-pixel digits, 20000-gene cells — but you can't see it. In this video you will learn about three very common methods for data dimensionality reduction: PCA, t-SNE and In my last video I presented python code in COLAB for a If you understand the main ideas of how In this video, we will cover the similarities and differences between PCA, t-SNE, Construct a topology-aware low-dimensional embedding. Learn the basics about making a custom map in This talk will present a new approach to dimension reduction called