Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Transfer learning is a technique where a model developed for one task is reused as the starting point for a model on a second task. A primary benefit of this approach is that it significantly reduces the need for large datasets for new tasks. When you leverage knowledge from a pre-trained model, you can apply the features and patterns it has already learned to a new, possibly related, task. This is particularly advantageous when data is scarce or difficult to obtain for the new task.

By enabling models to utilize existing knowledge, transfer learning allows for effective generalization even with limited data. This efficiency is especially important in fields like image recognition or natural language processing where obtaining large labeled datasets can be resource-intensive and costly. As a result, transfer learning promotes faster model development cycles and helps in achieving better performance with less data, which is why it stands out as a primary benefit in the context of artificial intelligence programming.