Transfer learning is a powerful technique in artificial neural networks that allows a model to leverage knowledge gained from one task and apply it to another related task. Instead of training a model from scratch, which can be time-consuming and requires a lot of data, transfer learning uses pre-trained models that have already learned features from a large dataset. For example, a model trained on thousands of images to recognize general objects can be fine-tuned to identify specific types of objects, like dogs or cars, with a smaller dataset. This speeds up the training process and often leads to better performance because the model starts with a solid foundation of knowledge. Transfer learning is especially useful in fields like image recognition and natural language processing, where large amounts of data can be difficult to gather.
