Artificial Neural Networks (ANNs) are a subset of machine learning algorithms inspired by the biological neurons found in the human brain that are designed to recognize patterns and make predictions based on data.

ANNs consist of layers of interconnected nodes (artificial neurons) that process and transmit information through a complex network of weighted connections. ANNs are used in a wide range of applications, including image and speech recognition, natural language processing, predictive analytics, and robotics, among others.

The learning process of ANNs involves adjusting the weights of the connections between the neurons based on the error between the predicted and actual outputs, using algorithms such as backpropagation. ANNs have shown impressive performance in many tasks, and their ability to learn from large datasets has made them a popular tool in the field of artificial intelligence.

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