Having set the context and basics of ML and deep learning in Chapter 1 to Chapter 3 of the book, this chapter began the second phase of building the foundations of transfer learning. Before diving into actual use cases, it is imperative that we formalize our understanding of transfer learning and learn about different techniques and research, and the challenges associated with it. Throughout this chapter, we have presented the fundamentals behind the concept of transfer learning, how it has evolved over the years, and why it was required in the first place.

We began by understanding transfer learning in the broader context of learning algorithms and their associated advantages. We then discussed various strategies for understanding, applying, and categorizing transfer learning methods. Transfer learning in the context of deep learning was the next topic discussed, to set the tone for the rest of the chapter. We discussed different transfer learning methodologies, such as feature-extraction and fine-tuning, that are associated with deep transfer learning. We also presented famous pretrained models and popular applications of transfer learning using deep learning systems. Deep learning has proven to be very successful in recent years, and thus a lot of research is being done into using transfer learning in this space.

We briefly discussed different variants of deep transfer learning, such as domain adaptation, domain confusion, multitask learning, one-shot learning, and zero-shot learning. We concluded the chapter by presenting the challenges, such as negative transfer and transfer bounds, associated with transfer learning. Throughout this chapter, we have outlined various research publications and links associated with transfer learning, and we encourage readers to explore them for more information. This chapter acts as a pointer and an overview of the current transfer learning landscape. Stay tuned for more details in the next chapter where we will get to some hands-on exercises related to Transfer Learning.