Deep Learning is a subset of machine learning that uses multi-layered artificial neural networks to learn hierarchical representations of data, enabling computers to automatically discover patterns and features without explicit programming. It powers modern AI applications from computer vision and natural language processing to speech recognition and autonomous systems. The field has evolved from basic feedforward networks to sophisticated architectures like transformers and diffusion models, with gradient-based optimization and backpropagation remaining the fundamental learning mechanisms. Understanding the trade-offs between model capacity, computational efficiency, and generalization is critical for practitioners building production systems.