In the architecture of an artificial neural network, a hidden layer is an intermediate layer located between the input layer and the output layer. Unlike the input layer, which receives raw data, and the output layer, which produces the final result, neurons in a hidden layer are not directly exposed to the external input or output. Instead, they receive inputs from the preceding layer (either the input layer or another hidden layer), perform computations (typically a weighted sum of inputs followed by an activation function), and then pass their outputs to the subsequent layer. A neural network can have one or many hidden layers, with deep learning referring to networks with multiple hidden layers. These layers are crucial because they enable the network to learn complex patterns, non-linear relationships, and abstract representations of the input data, which are essential for solving sophisticated tasks like image recognition, natural language processing, and anomaly detection.