In neural networks, a hidden layer refers to a layer of nodes (neurons) that sits between the input layer and the output layer. These hidden layers play a crucial role in the model's ability to learn and represent complex relationships within the data.

The architecture of a neural network is composed of three main types of layers:

• Input Layer: This layer consists of nodes that represent the input features of the data. Each node corresponds to a feature, and the values of these nodes are the input values.
• Hidden Layers: Hidden layers are intermediate layers between the input and output layers. Each node in a hidden layer receives input from the nodes in the previous layer and produces output for the nodes in the next layer. The term "hidden" comes from the fact that these layers are not directly observable in the input or output data; they serve as internal representations that the network learns during training.
• Output Layer: The output layer produces the final output of the neural network. The number of nodes in the output layer depends on the nature of the problem (e.g., binary classification, multi-class classification, regression).

The inclusion of hidden layers allows neural networks to model complex, nonlinear relationships in the data. Each node in a hidden layer applies a weighted sum of its inputs, followed by an activation function. The weights and biases associated with the connections between nodes are learned during the training process.

The depth (number of hidden layers) and width (number of nodes in each hidden layer) of a neural network's architecture can vary based on the complexity of the problem at hand. Deep neural networks, those with multiple hidden layers, have become particularly popular for tasks such as image recognition, natural language processing, and many other domains where intricate patterns need to be captured in the data.