Deep Learning Can Recognize Complex Relationships

For more complex problems, simple linear models are insufficient. A way out is offered by models with several nonlinear layers (operators), which can represent arbitrary “curved” relationships between inputs and outputs. This chapter describes the properties of such deep neural networks and shows how to find the optimal parameters using the backpropagation method. It then discusses the problem of overfitting and how it can be solved using regularization methods. Finally, an overview of the different types of deep neural networks is given and methods for finding a network structure are outlined.