Which term refers to the layers between input and output that enable learning?

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

Answer: (A)

Hidden layers are the layers between the input and output in a neural network. They enable learning by transforming raw input into progressively more abstract representations through weighted connections and nonlinear activation functions. Each hidden layer extracts features, and stacking multiple layers lets the network capture complex patterns and relationships in the data. During training, the weights in these layers are adjusted to improve predictions, which is how learning occurs. The input layer simply passes data in, and the output layer produces the final results; there isn’t a standard term like 'data layer' for the intermediate stages.