Data Augmentation
Data augmentation is a technique in machine learning and computer vision that artificially expands a dataset by applying transformations to existing data, such as rotations, flips, or color adjustments. It helps improve model generalization and performance by increasing data diversity without collecting new samples, reducing overfitting and enhancing robustness to variations in real-world data.
Developers should learn data augmentation when working with limited or imbalanced datasets, especially in computer vision, natural language processing, or audio processing tasks. It is crucial for training deep learning models in fields like image classification, object detection, and medical imaging, where data scarcity or high annotation costs are common, as it boosts accuracy and reduces the need for extensive manual data collection.