Augmented tuning typically refers to techniques used to enhance the performance of machine learning models, particularly deep learning models, by leveraging additional information or methodologies beyond standard training practices. While the term may not have a universally accepted definition, it generally encompasses strategies such as: 1. **Data Augmentation**: This involves creating new training examples by applying transformations to existing data, such as rotations, flips, scaling, or color adjustments.
New to topics? Read the docs here!