What is “data augmentation” in deep learning?

Answer:

Explanation:

Data augmentation is a technique used in deep learning to artificially increase the size of the training dataset by applying transformations, such as rotations, flips, scaling, or color changes, to the original data. These transformations create new, modified versions of the existing data, which helps the model learn more diverse features.

This approach is particularly useful in image classification tasks, where creating additional training samples from limited data can significantly improve the model’s performance and prevent overfitting. Data augmentation allows the model to generalize better to unseen data.

By introducing variations in the training data, data augmentation makes the model more robust and helps it learn patterns that are invariant to transformations, such as changes in lighting, orientation, or scale.