Deep Learning (DL) is a subset of machine learning that uses neural networks to analyze and interpret complex data. It mimics the way the human brain processes information, allowing computers to learn from large amounts of data without explicit programming. DL is particularly effective in tasks such as image and speech recognition, natural language processing, and autonomous driving. The architecture of deep learning models typically consists of multiple layers of interconnected nodes, known as neurons. Each layer extracts different features from the input data, enabling the model to make accurate predictions or classifications. Popular frameworks for developing DL models include TensorFlow and PyTorch.