A deep neural network (DNN) is an artificial neural network (ANN) with multiple hidden layers that can learn complex nonlinear relationships between inputs and outputs. By contrast, a shallow neural network (SNN) has only one or two hidden layers and is limited to learning linear relationships between inputs and outputs.
For example, a DNN could be used to predict the stock market based on a variety of inputs, such as news headlines, economic indicators, and historical data. A shallow neural network, on the other hand, could only be used to predict the stock market based on a single input, such as the S&P 500 index.