The purpose of artificial neural networks in deep learning is to model complex patterns and relationships within data. Inspired by the human brain, these networks consist of interconnected nodes (neurons) that process input data and learn through training using large datasets. They excel in tasks such as image recognition, natural language processing, and predictive analytics, allowing systems to learn autonomously and improve performance over time without explicit programming.