In the previous chapters, we developed deep learning networks and explored various application examples related to image data. One major difference compared to what we will be discussing in this chapter is that, in the previous chapters, we developed models from scratch.
Transfer learning can be defined as an approach where we reuse what a trained deep network has learned to solve a new but related problem. For example, we may be able to reuse a deep learning network that's been developed to classify thousands of different fashion items to develop a deep network to classify three different types of dresses. This approach is similar to what we can observe in real life, where a teacher transfers knowledge or learning gained over the years to students or a coach passes on learning or experience to new players. Another...
