What is transfer learning and in what scenarios is it useful?

Transfer learning is a machine learning technique where a model developed for a particular task is reused as the starting point for a model on a second related task. This approach is particularly useful when there is limited labeled data for the new task, allowing practitioners to leverage the knowledge captured in a model trained on a larger dataset. Common scenarios include using pretrained models from large image datasets (like ImageNet) for image classification tasks or leveraging natural language processing models (like BERT) for specific NLP applications. Transfer learning can significantly reduce training time and improve performance in tasks where the available data is scarce.