Synthetic Data Generation
Synthetic Data Generation is the process of creating artificial data that mimics the statistical properties of real-world data. This technique is used to generate large amounts of data that can be used for testing, training, and validating machine learning models without compromising the privacy of individuals or organizations. Synthetic data can be generated using various methods such as generative adversarial networks (GANs), variational autoencoders (VAEs), and Monte Carlo simulations. The generated data can be used to augment existing datasets or create entirely new datasets. Synthetic data generation is particularly useful in situations where real-world data is scarce, expensive, or difficult to obtain. It also helps to mitigate the risk of data breaches and protect sensitive information.
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