In this chapter, you learned the basics of neural networks, more specifically, what a perceptron is, what a multilayer perceptron is, how to define neural networks in Keras, how to progressively improve metrics once a good baseline is established, and how to fine-tune the hyperparameter's space. In addition to that, you now also have an intuitive idea of what some useful activation functions (sigmoid and ReLU) are, and how to train a network with backpropagation algorithms based on either gradient descent, on stochastic gradient descent, or on more sophisticated approaches, such as Adam and RMSprop.
In the next chapter, we will see how to install Keras on AWS, Microsoft Azure, Google Cloud, and on your own machine. In addition to that, we will provide an overview of Keras APIs.
