In this section, you will first learn about the representations and concepts of deep learning such as forward propagation, backpropagation, and gradient descent. We will not delve deeply into these concepts, as it isn't required for this book. However, the coverage will essentially help anyone who wants to apply deep learning to a problem.
We then will move on to implementing neural networks using Keras. Also, we will stick to the simplest case, which is a neural network with a single hidden layer. You will learn how to define a model in Keras, choose the hyperparameters, and then train your model. At the end of this section, you will have the opportunity to practice what you have learned by implementing a neural network in Keras to perform classification on a dataset and observe how neural networks outperform simpler models such as logistic regression.
