Transfer learning is a machine learning technique that involves taking a pre-trained model—trained on a large dataset—and fine-tuning it for a specific, often smaller, task. This approach is particularly useful when labeled data is scarce or expensive to obtain. By transferring knowledge from one domain to another, transfer learning accelerates the training process and improves model performance. It is widely used in deep learning applications, especially in computer vision and natural language processing, where models like Convolutional Neural Networks (CNNs) and Transformers can be adapted to new tasks with minimal data. This efficiency makes transfer learning a valuable strategy for leveraging existing knowledge in AI.