<ul><li>Synthetic data refers to artificially generated information that mimics the statistical properties of real-world data.</li><li>It is used in various applications such as data augmentation, bias mitigation, simulation-based training, privacy preservation, medical imaging, fraud detection, risk analysis, personalization, and demand forecasting.</li><li>Specific use cases of synthetic data include autonomous vehicles, anti-fraud models in finance, medical AI models, e-commerce recommendation systems, and digital twins in manufacturing.</li><li>Despite its benefits, synthetic data poses challenges in terms of accuracy, validation, and ethical considerations.</li></ul>

Understanding Synthetic Data: Applications and Use Cases in the Modern Era

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