What are the layers between the input and output called, which process information within a neural network?

What are the layers between the input and output called, which process information within a neural network?

• A. Input Layer
• B. Hidden Layers
• C. Output Layer
• D. Weights

Answer: (B)

Hidden layers are the parts of a neural network that sit between the input and the output and do the processing. They transform the raw data as it flows through the network, applying linear combinations of inputs using weights and biases, then passing the result through nonlinear activation functions. This layering lets the model build increasingly abstract representations of the data, from simple patterns in early hidden layers to more complex features in deeper layers. The input layer simply provides the initial data, and the output layer produces the final prediction, so they are not doing the intermediate processing. Weights are the adjustable parameters inside the connections that the network learns during training; they aren’t layers themselves.

Hidden layers are the parts of a neural network that sit between the input and the output and do the processing. They transform the raw data as it flows through the network, applying linear combinations of inputs using weights and biases, then passing the result through nonlinear activation functions. This layering lets the model build increasingly abstract representations of the data, from simple patterns in early hidden layers to more complex features in deeper layers. The input layer simply provides the initial data, and the output layer produces the final prediction, so they are not doing the intermediate processing. Weights are the adjustable parameters inside the connections that the network learns during training; they aren’t layers themselves.