An artificial neural network consists of layers of interconnected nodes (neurons) that process input data, apply transformations through weighted connections, and pass results to subsequent layers until an output is produced. Networks learn by adjusting the weights of connections during training to minimize prediction errors. Neural networks are the foundation of deep learning and underpin virtually all modern AI applications — from language models to image classifiers to recommendation systems.