Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issu

Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can cre

The aim of this book is to give the reader a detailed introduction to the different approaches to generating multiply imputed synthetic datasets. It describes a

Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learnin

This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for year