In this chapter, you will learn how to implement your first neural network using Keras. This chapter covers the basics of deep learning and will provide you with the foundation necessary to build highly complex neural network architectures. We start by extending the logistic regression model to a simple single-layer neural network and then proceed to more complicated neural networks with multiple hidden layers. In this process, you will learn about the underlying basic concepts of neural networks, including forward propagation for making predictions, computing loss, backpropagation for computing derivative of loss with respect to model parameters, and finally gradient descent for learning optimal parameters for the model. You will also learn about the various choices available to build and train a neural network in terms of activation functions, loss functions, and optimizers.
Furthermore, you will learn how to evaluate your model while understanding issues such as overfitting...
