Artificial neural networks (ANNs) are state-of-the-art machine learning architectures modeling neurons and their connections through weights and biases. ANNs serve as universal function approximators, meaning that a sufficiently complex neural network can learn almost any function in any dimension. This flexibility, combined with backpropagation and a learning algorithm, enables one to learn unknown functions with astonishing accuracy. This chapter explains key architectures, such as fully connected, convolutional, and recurrent networks, along with essential concepts like forward propagation, backpropagation, and regularization techniques. Designed for readers new to the field, it provides clear explanations of the basic algorithms as well as examples to demonstrate how these networks can model complex non-linear relationships in physics and mechanics, e.g., deployable as surrogate models.