Practical synthetic data generation balancing privacy and the broad availability of data
Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media
[2020]
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631300306719 |
Tabla de Contenidos:
- Introducing synthetic data generation
- Implementing data synthesis
- Getting started: distribution fitting
- Evaluating synthetic data utility
- Methods for synthesizing data
- Identity disclosure in synthetic data
- Practical data synthesis.
