The previous chapters have introduced several types of methods for classifying and regressing, given some datasets, often in terms of multidimensional feature vectors. In this chapter, we turn to a different approach: artificial neural networks (ANNs). ANNs are at the root of many state-of-the-art deep learning algorithms. Although a few of the ANNs in the literature were fundamentally motivated by biological systems and some even came with hardware implementations, such as the original McCulloch–Pitts neuron (McCulloch et al., 1943), the vast majority were designed as simple computational procedures with little direct biological relevance. For the sake of practicality, the presentation of this chapter will mostly concern mostly the algorithmic aspects of ANNs.