3-2. Transfer Learning:
Transfer learning is a machine learning strategy that uses a pre-trained model as a starting point to solve a new, but related, problem. Instead of training a model from scratch, transfer learning allows to fine-tune the pre-trained model to the new task or to use it as a feature extractor to train a new classifier. This approach saves time and resources, and can result in improved performance on the new task. Transfer learning is commonly applied to computer vision and natural language processing problems, using pre-trained models on large datasets as the starting point for new tasks.