In machine learning, transfer learning occurs when an existing algorithm takes on a different (but similar) task. This is what transfer learning is about, its benefits, and its applications.

In transfer learning, developers reuse an algorithm designed for a specific purpose for a different task. The new algorithm applies what it already knows to do the new work.

For example, if you had an algorithm that could identify photos of dogs, you could easily modify it to identify cats. You could build on the algorithm to create one that could identify any animal.

Artificial intelligence (AI) programs rely on various machine learning algorithms to become better and faster at performing their intended task. Transfer learning is not really a type of machine learning, but rather a method used within the field. Transfer learning also has applications outside of machine learning.