What are the layers between input and output that are not directly visible called?

• A. Input layer
• B. Output layer
• C. Hidden layers
• D. Data layer

Answer: (C)

Hidden layers are the layers between input and output that aren’t directly visible. In a neural network, the input layer passes the raw data in, the output layer delivers the final prediction, and the hidden layers in between transform that data through weighted connections and nonlinear activations. Each hidden layer learns intermediate representations—progressively extracting features and patterns from the data—so deeper networks can model more complex relationships. The term “hidden” reflects that these internal computations aren’t observed directly from the outside. The input layer and the output layer are the visible ends, and while “data layer” isn’t standard terminology for this concept, it doesn’t describe the internal processing layers.