What does transfer learning allow you to do in machine learning?

Transfer learning is a powerful technique in machine learning that enables practitioners to leverage pre-trained models, which have been trained on large datasets, for similar tasks. This approach allows you to adapt an existing model rather than starting from scratch, which can be time-consuming and data-intensive.

By using a pre-trained model, you can significantly reduce the amount of training data required for your new task. This is especially advantageous in scenarios where obtaining labeled data is either challenging or expensive. The model has already learned useful features from the original dataset, and you can fine-tune it on your specific dataset, allowing it to perform well even with limited data.

This technique is widely used in domains such as image classification and natural language processing, where large models, like convolutional neural networks or transformer models, have been extensively trained. Researchers and developers take these models, adjust them for the new task by retraining some layers while keeping others fixed, and achieve good performance quickly and efficiently.