Artificial Neural Networks (ANNs) are a type of computational model inspired by the structure and functioning of the human brain. These networks are composed of interconnected nodes, or neurons, that process information through a series of mathematical computations. ANNs are used in various applications such as pattern recognition, image and speech recognition, natural language processing, and predictive modeling. ANNs can be trained using algorithms like backpropagation, where the network adjusts the strength of connections between neurons to minimize errors in prediction or classification tasks. The structure and complexity of ANNs can vary, ranging from simple feedforward networks to more complex recurrent or convolutional networks. Overall, artificial neural networks have become a powerful tool in the field of machine learning and artificial intelligence, enabling computers to learn from data, make predictions, and solve complex problems in a way that mimics human brain functioning.