Model Evaluation

In this section, we will move on to multi-layer or deep neural networks while learning about techniques for assessing the performance of a model. As you may have already realized, there are many hyperparameter choices to be made when building a deep neural network.

Some of the challenges of applied deep learning include how to find the right values for the number of hidden layers, the number of units in each hidden layer, the type of activation function to use for each layer, and the type of optimizer and loss function for training the network. Model evaluation is required when making these decisions. By performing model evaluation, you can say whether a specific deep architecture or a specific set of hyperparameters is working poorly or well on a particular dataset, and therefore decide whether to change them or not.

Furthermore, you will learn about overfitting and underfitting. These are two very important issues that can arise when building and training...