Deep learning is a branch of machine learning that uses artificial neural networks with many layers to automatically learn useful representations from data. Instead of relying on hand designed features, deep models learn hierarchies of increasingly abstract features directly from raw inputs such as images, audio or text.

The core building block is the artificial neuron, which computes a weighted sum of its inputs followed by a nonlinear activation function. By stacking many layers of these units, the network can approximate very complex functions. Training is typically done with gradient based optimization, especially stochastic gradient descent and its variants, combined with backpropagation to compute gradients efficiently.

Convolutional neural networks are central for image and video processing. They use local receptive fields, weight sharing and pooling to exploit spatial structure, enabling tasks like image classification, object detection and segmentation. Recurrent and sequence models, including architectures inspired by long short term memory and attention mechanisms, handle time series and natural language, learning dependencies over long contexts.

Key technical tools include regularization methods such as dropout and weight decay, careful initialization and normalization strategies that stabilize training, and large scale computation on GPUs or specialized hardware. Deep learning has driven major advances in speech recognition, machine translation, medical image analysis, game playing and generative modeling, where models can synthesize realistic images, audio and text.

Despite its successes, deep learning faces challenges such as data hunger, interpretability, robustness to adversarial inputs and high energy consumption. Ongoing research seeks more efficient architectures, better training objectives, improved uncertainty estimation and closer integration with prior knowledge and symbolic reasoning.