Which layer in a neural network is not directly connected to input or output?

The hidden layer in a neural network is crucial because it processes inputs received from the input layer and transforms them into outputs that are then sent to the output layer. Unlike the input layer, which directly interacts with the external data, and the output layer, which provides the final predictions or classifications, the hidden layer acts as a bridge between these two.

In a typical neural network architecture, the hidden layers perform various functions, such as activation and transformation, allowing the network to learn complex patterns and representations within the data. This is achieved through the use of weights, biases, and activation functions, which are not present in the input or output layers. The hidden layers thus represent the internal workings of the neural network, making them essential for learning and decision-making processes.

This characteristic distinguishes the hidden layer from the other types of layers, as it does not have any direct connections to the original input or the final output, but instead plays an intermediary role in the data processing workflow.