Artificial Intelligence Programming Practice Exam 2025 – The All-in-One Guide to Mastering AI Programming!

Transfer learning in machine learning refers to the process where a model that has been developed to solve one particular task is utilized or modified to perform a different but related task. This approach is particularly beneficial when there is a limited amount of data available for the new task, as it allows the model to leverage the knowledge and features gained from training on the original task.

For instance, a deep learning model trained on a large dataset for image classification can be adapted to a similar but distinct classification task with fewer data requirements. By reusing the learned representations, the model can significantly reduce training time and improve performance as it begins the learning process from a more informed state rather than from scratch.

The other options do not accurately describe transfer learning. For example, techniques for data encryption pertain to securing information, while increasing model complexity or optimizing hyperparameters are distinct aspects of model training and do not involve reusing knowledge from previously learned tasks.