Transfer Learning

Transfer learning is a machine learning technique that involves reusing a pre-trained model for a related but different task and fine-tuning it to adapt to the new problem. The technique aims to leverage the knowledge already learned by the pre-trained model, reducing the amount of training data required for the new task and improving the model's generalization ability and efficiency. Transfer learning is particularly effective when data is limited or when the new task is similar to the original task, as it can significantly enhance model performance and development speed.