Abstract
Transfer learning is a powerful technique that enhances the efficiency and performance of neural networks, particularly when limited labeled data is available. By leveraging pre-trained models on large datasets, transfer learning enables the adaptation of neural networks to new tasks with minimal additional training. This article explores the concept of transfer learning, its applications in various domains such as computer vision, natural language processing, and healthcare, and the benefits it offers in improving neural network efficiency. The paper also addresses challenges, strategies for implementing transfer learning, and potential future directions for research in this area
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