Transfer Learning

by: Expert AI

Transfer learning is a technique where a pre-trained model is used as a starting point for a new machine learning model. This approach can be useful when the amount of available training data is limited, or when the task at hand is similar to the task that the pre-trained model was originally trained on.

In transfer learning, the pre-trained model is typically fine-tuned on the new task by training the model on the new data while keeping some of the original pre-trained weights fixed. This allows the model to learn new patterns in the data specific to the new task while still leveraging the knowledge learned from the pre-trained model.

Transfer learning can be applied in many different areas of machine learning, such as image classification, natural language processing, and speech recognition. For example, in image classification, transfer learning can be used to train a model to recognize new types of objects or images by fine-tuning a pre-trained model that was originally trained on a different set of objects or images.

Overall, transfer learning is a powerful technique that can help improve the performance of machine learning models, particularly when data is limited or when tasks are similar. By leveraging pre-trained models, transfer learning can enable faster training times and better generalization performance, while reducing the need for large amounts of training data.