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Transfer Learning is a powerful machine learning technique that enables researchers and developers to leverage pre-trained models to accelerate AI development in various domains. This article explores the concept of Transfer Learning, its underlying principles, and its applications across various domains such as computer vision and natural language processing. Transfer Learning has been successfully applied in various domains and is a valuable technique in artificial intelligence and machine learning, allowing researchers and developers to build on the knowledge of pre-trained models to tackle new tasks more efficiently.