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Data Augmentation
Data augmentation creates variations of existing data to improve the diversity and quality of training datasets for machine learning models. This process strengthens model performance by simulating diverse scenarios, helping reduce overfitting and improving generalization. Techniques include flipping, rotating, or cropping images, adding noise to audio files, and modifying text through paraphrasing or replacing words. By expanding datasets, data augmentation allows models to learn from more varied examples, leading to better accuracy on unseen data. It is widely used in computer vision, natural language processing, and speech recognition, where collecting large datasets can be expensive or time-consuming. This method is an affordable and practical way to enhance the reliability and performance of machine learning systems.