Deep Feedforward Networks

In this chapter, you will build our first deep learning network—deep deedforward networks (DFN). We will begin by discussing the evolutionary history of deep feedforward networks and then discuss the architecture of DFN. In any classification task, DFN plays an integral role. Apart from supporting the classification tasks, DFN standalone can be used both for regression and classification. Any deep learning network has a lot of elements like loss function, gradients, optimizers, and so on coming together to train the network. In this chapter, we will discuss these essential elements in detail. These elements will be common to all kinds of deep learning networks we are going to see in this book. We will also be demonstrating how to bring up and preprocess the data for training a deep learning network. You may find things a little difficult to understand at first, but eventually, you will get it. So, just hold on to it! After completing this chapter, you will have learned about the following things:

• Architecture of DFN
• Loss function
• Gradient descent
• Backpropagation
• Overfitting and regularization
• Coding your first DFN

Let's start with the evolution history of DFNs!