When To Use Transfer Learning. For example knowledge gained while learning to recognize cars can be used to some extent to recognize trucks. Next you must develop a skillful model for this first task.
Transfer learning solved this problem by allowing us to take a pre trained model of a task and use it for others. For example a neural network trained on object recognition can be used to read x ray scans. For example knowledge gained while learning to recognize cars can be used to some extent to recognize trucks.
Transfer learning is mostly used in computer vision and natural language processing tasks like sentiment analysis due to the huge amount of computational power required.
Today transfer learning is at the heart of language models like embeddings from language models elmo and bidirectional encoder representations from transformers bert which can be used for any downstream task. This usually occurs when the two skills in question are similar in some way. You must select a related predictive modeling problem with an abundance of data where there is some relationship in the input. Transfer learning make use of the knowledge gained while solving one problem and applying it to a different but related problem.