Data Augmentation involves artificially diversifying training data by applying multiple modifications, such as rotation, cropping, or noise addition, to generate new instances. As a result, the dataset is improved, and machine learning models’ capacity to generalize is enhanced.
Data Augmentation
SHARE
Related Links
Customer Lifetime Value (CLV) is no longer just a metric—it’s a strategic asset that can shape…
A constant challenge businesses across industries face is building a personal connection with their audience in…
