Summary

In this chapter, you extended your knowledge of deep learning, from understanding the common representations and terminology to implementing them in practice through exercises and activities. You learned how forward propagation in neural networks works and how it is used for predicting outputs, how the loss function works as a measure of model performance, and how backpropagation is used to compute the derivatives of loss functions with respect to model parameters.

You also learned about gradient descent, which uses the gradients that are computed by backpropagation to gradually update the model parameters. In addition to basic theory and concepts, you implemented and trained both shallow and deep neural networks with Keras and utilized them to make predictions about the output of a given input.

To evaluate your models appropriately, you split a dataset into a training set and a test set as an alternative approach to improving network evaluation and learned the reasons...