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Practical Synthetic Data Generation
book

Practical Synthetic Data Generation

by Khaled El Emam, Lucy Mosquera, Richard Hoptroff
May 2020
Beginner to intermediate
163 pages
4h 31m
English
O'Reilly Media, Inc.
Content preview from Practical Synthetic Data Generation

Chapter 1. Introducing Synthetic Data Generation

We start this chapter by explaining what synthetic data is and its benefits. Artificial intelligence and machine learning (AIML) projects run in various industries, and the use cases that we include in this chapter are intended to give a flavor of the broad applications of data synthesis. We define an AIML project quite broadly as well, to include, for example, the development of software applications that have AIML components.

Defining Synthetic Data

At a conceptual level, synthetic data is not real data, but data that has been generated from real data and that has the same statistical properties as the real data. This means that if an analyst works with a synthetic dataset, they should get analysis results similar to what they would get with real data. The degree to which a synthetic dataset is an accurate proxy for real data is a measure of utility. We refer to the process of generating synthetic data as synthesis.

Data in this context can mean different things. For example, data can be structured data, as one would see in a relational database. Data can also be unstructured text, such as doctors’ notes, transcripts of conversations or online interactions by email or chat. Furthermore, images, videos, audio, and virtual environments are types of data that can be synthesized. Using machine learning, it is possible to create realistic pictures of people who do not exist in the real world.

There are three types of synthetic data. ...

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Publisher Resources

ISBN: 9781492072737Errata Page