Humans have an inherent ability to transfer knowledge across tasks. What we acquire as knowledge while learning about one task, we utilize in the same way to solve related tasks. The more related the tasks, the easier it is for us to transfer, or cross-utilize, knowledge. Machine learning and the deep learning algorithms, discussed so far, have been traditionally designed to work in isolation. These algorithms are trained to solve specific tasks. The models have to be rebuilt from scratch once the feature-space distribution changes. Transfer learning is the idea of overcoming the isolated learning paradigm and utilizing knowledge acquired for one task to solve related ones. In this chapter, we will introduce the concept of transfer learning and concentrate on aspects of the context of deep learning. This chapter will cover the following topics:

• Introduction to transfer learning
• Transfer learning strategies
• Transferring knowledge with deep learning
• Types of deep transfer learning
• Challenges of transfer learning