Libro

Descripción

Synthetic Data Generation for Machine Learning: Privacy, Simulation, and Model Training explores synthetic data as an applied discipline: a sequence of questions that must be resolved before a technical procedure becomes a defensible decision. Readers work through data construction, modeling, validation, failure analysis, and governance. Each chapter uses a specific setting and asks the reader to identify what the evidence actually supports. The book can support a short university course, an intensive workshop, or independent study. Chapters are designed to be read in order, but instructors may pair an earlier foundations chapter with a later applied investigation. Written exercises deliberately require argument, calculations, and experimental design rather than recalling definitions.