May 2017
Intermediate to advanced
294 pages
7h 33m
English
Content preview from Apache Spark 2.x Cookbook
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Dimensionality reduction with principal component analysis
Dimensionality reduction is the process of reducing the number of dimensions or features. A lot of real data contains a very high number of features. It is not uncommon to have thousands of features. So we need to drill down to features that matter.
Dimensionality reduction serves several purposes, such as:
- Data compression
- Visualization
When the number of dimensions is reduced, it reduces the disk and memory footprint. Last but not least, it helps algorithms to run faster. It also helps reduce highly correlated dimensions to one.
Humans can only visualize three dimensions, but data has access to a much higher number of dimensions. Visualization can help find hidden patterns in ...
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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.