What is transfer learning?

Transfer learning is a machine learning technique where a model trained on one task is repurposed as the starting point for a model on a second task. It leverages the knowledge gained from the initial task to improve the learning efficiency and performance on the new, related task. It is commonly used when the target task has limited labeled data, or when the computation cost of training from scratch is high. It can significantly reduce training time and improve model accuracy, especially in areas like image recognition, natural language processing, and speech recognition.