TensorFlow and TF.Keras support a wide availability of prebuilt and pretrained models. Pretrained models can be used as is, while prebuilt models can be trained from scratch. By replacing the task group, pretrained models can also be reconfigured to perform any number of tasks. The process of replacing or reconfiguring the task group with retraining is called transfer learning.
In essence, transfer learning means transferring the knowledge for solving one task to solving another task. The benefit of transfer learning versus training a model from scratch is that the new task can be trained faster and with less data. Think of it as a form of reuse: we are reusing the model with its learned weights.