Deep learning is a subset of machine learning that uses artificial neural networks to model and solve complex problems. Inspired by the human brain, these networks consist of layers of interconnected nodes (neurons) that process data hierarchically. Deep learning excels at recognizing patterns in large datasets, making it ideal for tasks like image and speech recognition, natural language processing, and autonomous driving.

Unlike traditional algorithms that require manual feature extraction, deep learning models automatically learn relevant features from raw data. This is achieved through multiple layers—each transforming the input into increasingly abstract representations. Training these models involves feeding them vast amounts of labeled data and adjusting internal parameters using optimization techniques like backpropagation and gradient descent.

Deep learning has driven major advances in AI, powering technologies such as virtual assistants, recommendation systems, and medical diagnostics. However, it requires significant computational resources and large datasets, and its models often lack interpretability, posing challenges in transparency and trust.