January 2025
Intermediate to advanced
614 pages
15h 37m
English
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5.3 t-SNE and UMAP for High-Dimensional Data
When dealing with high-dimensional datasets, the challenge of reducing dimensionality while maintaining meaningful structure becomes paramount. Although Principal Component Analysis (PCA) proves effective for linear transformations, it often falls short in capturing the intricate non-linear relationships inherent in complex data structures. This limitation necessitates the exploration of more sophisticated techniques.
Enter t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), two advanced non-linear dimensionality reduction techniques. These methods are specifically engineered to visualize high-dimensional data in lower-dimensional spaces, typically ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.