Transfer Learning

Transfer learning is a machine learning technique where knowledge gained from training a model on one task is reused to improve performance on a related task. Instead of starting from scratch, a pre-trained model is adapted with new data, saving time, computational resources, and the need for massive datasets. This approach is especially effective in areas like image recognition, natural language processing, and speech analysis, where foundational models can be fine-tuned for specialized purposes. By leveraging previously learned patterns, transfer learning allows AI systems to achieve high accuracy even with limited data. It plays a crucial role in modern AI development, enabling faster innovation and broader accessibility to advanced model capabilities.