Example Concept: Data augmentation involves artificially increasing the size of your training dataset by applying transformations such as rotation, scaling, or flipping to existing data. Transfer learning leverages pre-trained models on similar tasks, allowing you to fine-tune them with your limited dataset. Cross-validation helps in assessing model performance more reliably by splitting the data into multiple training and validation sets.