What is transfer learning, and how can it be used to improve the performance of machine learning models?

Transfer learning is a technique in machine learning where a pre-trained model is used as a starting point for solving a different but related problem. The idea is that the knowledge and feature representations learned by the pre-trained model can be transferred to the new problem, allowing for faster and more accurate training. The pre-trained model is typically trained on a large dataset and has learned to identify features that are relevant to a specific task. By leveraging this knowledge, we can reduce the amount of training required for a new model and improve its accuracy. Transfer learning is particularly useful when the new task has a smaller dataset or when training a model from scratch is compu....