In the previous recipe, Implementing a single-layer neural network, we implemented a simple baseline neural network for a classification task. Continuing with that model architecture, we will create a deep neural network. A deep neural network consists of several hidden layers that can be interpreted geometrically as additional hyperplanes. These networks learn to model data in complex ways and learn complex mappings between inputs and outputs. 

The following diagram is an example of a deep neural network with two hidden layers:

In this recipe, we will learn how to implement a deep neural network for a multi-class classification problem.