A Deep Neural Network (DNN) is a type of artificial neural network built with multiple layers of interconnected computational nodes. Each layer transforms the information it receives and passes increasingly refined representations to the next layer. Through training, the network adjusts the strength of connections between nodes so it can learn complex relationships in data without requiring every rule to be explicitly programmed.
DNNs are widely used when patterns are too complex for conventional algorithms, including image classification, speech recognition, fraud detection, recommendation systems, and natural language processing. For example, a DNN can learn visual features from pixels and use them to distinguish objects in photographs. Related concepts include deep learning, neural networks, machine learning, convolutional neural networks, and recurrent neural networks.
