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.