Transfer learning is a machine learning technique that leverages knowledge gained from one task to enhance the learning process of a related task. This approach is particularly beneficial in deep learning, where pre-trained models can be adapted for new applications, such as computer vision and natural language processing. By utilizing a model developed for a source task, transfer learning allows for improved efficiency and effectiveness, especially when data is scarce for the target task. This methodology not only accelerates the training process but also enhances model performance by building upon existing knowledge and skills acquired from previous experiences.