ABSTRACT
This chapter summarizes key points about Artificial Neural Networks (ANN) as well as describe use-cases in which ANNs play a major role in the automation of experimental data analysis. In the late 1940s, researchers explored the idea of creating mathematical models that could learn and perform tasks inspired by the way the brain works. Machine learning techniques such as ANNs involve two phases: training to construct a data-driven model and inference to use these models for prediction, with the training phase being the more computationally expensive. Over the years, ANNs have been used in a wide variety of applications, including pattern detection, image and speech recognition, natural language processing, and robotics. Over the past years, members of the Asimov Institute designed a series of charts in an attempt to lay down common notations and create a taxonomy that represents the avalanche of models.
