October 2017
Beginner to intermediate
572 pages
26h 1m
English
book
Machine Learning with R Cookbook - Second Edition
by AshishSingh Bhatia, Yu-Wei, Chiu (David Chiu)
Content preview from Machine Learning with R Cookbook - Second Edition
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Reducing dimensions with SVD
Singular Value Decomposition (SVD) is a type of matrix factorization (decomposition), which can factorize matrices into two orthogonal matrices and diagonal matrices. You can multiply the original matrix back using these three matrices. SVD can reduce redundant data that is linear dependent from the perspective of linear algebra. Therefore, it can be applied to feature selection, image processing, clustering, and many other fields. In this recipe, we will illustrate how to perform dimension reduction with SVD.
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.