January 2024
Intermediate to advanced
408 pages
13h 21m
English
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Chapter 10: Synthetic tabular data: copulas vs enhanced GANs
Abstract
In this chapter, you will learn how to create your own tabular synthetic data in Python, using two popular techniques: GANs and copulas. One example includes a real-life insurance data set: using copulas, you will be able to create an alternate (synthetic) data set that matches very well the distribution of the observations in your training set, including all the correlations. Another example is the diabetes data set; the goal is to predict cancer, and the context is supervised classification. You will learn how to synthesize this data set using GANs (generative adversarial networks). I also discuss data transformations, how to deal with missing data, and modern tools to assess ...
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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.