Artificial neural networks (ANNs) are used in machine learning to model and solve problems by mimicking the structure and function of the human brain. They consist of interconnected layers of nodes (neurons) that process input data through weighted connections.
ANNs are applied in tasks like regression, classification, and clustering. For instance, in image recognition, they learn patterns and features from training data to identify objects in unseen images. Variants like CNNs and RNNs extend their capabilities to specialized domains like computer vision and sequential data processing.
By automatically learning from data, ANNs eliminate the need for manual feature engineering, making them powerful tools for solving complex, non-linear problems in machine learning.