Transfer Learning

One of the most significant developments of the last decade in the machine learning (ML) space was the concept of transfer learning, and rightfully so. Transfer learning is the process of applying knowledge gained from solving a source task to a target task, which is a different but related task. This approach has proven not only effective in saving computational resources required to train a deep neural network but also in cases where the target dataset is limited in size. Transfer learning reuses learned features from a pre-trained model, enabling us to build better-performing models and attain convergence much faster. Because of its numerous benefits, transfer learning has become an area of extensive research, with several studies exploring the application of transfer learning across different domains, such as image classification, object detection, natural language processing, and speech recognition.

In this chapter, we will introduce the concept of transfer...