In this chapter, we have briefly discussed some basic deep learning concepts, and the reader should now understand what a computational sequential graph is and how it can be modeled using Keras. A deep architecture, in fact, can be seen as a sequence of layers connected to one another. They can have different characteristics and purposes, but the overall graph is always a directed structure that associates input values with a final output layer. Therefore, it's possible to derive a global loss function that will be optimized by a training algorithm.
We have presented Keras, which is a high-level framework that allows modeling and training complex deep-learning architectures. As an introductory example, we have shown the reader how to build an MLP that is able to solve the two spirals problem, and we have shown how to wrap a Keras model into a class that implements...
