Data augmentation is commonly used in machine learning tasks to address the problem of limited training data.
It generates new training examples by applying random transformations to the existing data.
It uses methods like flipping, rotating, zooming, cropping, or adding noise to generate the new data.
The purpose of data augmentation is to improve the model's generalization and reducing overfitting.
Data augmentation is particularly effective when the dataset is small or imbalanced
Data augmentation is typically applied on-the-fly during the training process.
Make sure the data augmentation do not introduce unrealistic variations that could mislead the model.
Several deep learning libraries and frameworks provide built-in support for data augmentation
Learn about Overfitting and underfitting:dealing with model complexity