Applying Transfer Learning to Network Models

In this chapter, we will talk about transfer learning methods, which are essential to reuse a model that was previously developed. We will see how we can apply transfer learning to the model created in Chapter 3, Building Deep Neural Networks for Binary Classification, as well as a pre-trained model from the DL4J Model Zoo API. We can use the DL4J transfer learning API to modify the network architecture, hold specific layer parameters while training, and fine-tune model configurations. Transfer learning enables improved performance and can develop skillful models. We pass learned parameters learned from another model to the current training session. If you have already set up the DL4J workspace for previous chapters, then you don't have to add any new dependencies in pom.xml; otherwise, you need to add the basic Deeplearning4j...