Introduction to Neural Networks.

Machine learning has become an essential tool for extracting regularities in the data and for making inferences. Neural networks, in particular, provide the scalability and flexibility that is needed to convert complex datasets into structured and well-generalizing models. Pretrained models have strongly facilitated the application of neural networks to images and text data. Application to other types of data, e.g., in physics, remains more challenging and often requires ad-hoc approaches. In this chapter, we give an introduction to neural networks with a focus on the latter applications. We present practical steps that ease training of neural networks, and then review simple approaches to introduce prior knowledge into the model. The discussion is supported by theoretical arguments as well as examples showing how well-performing neural networks can be implemented easily in modern neural network frameworks. [ABSTRACT FROM AUTHOR]