Hidden Layer Width

Hidden layer width is one of the key capacity indicators that determines how many different patterns a neural network can represent at once. Wider layers may provide greater representational power, but they also increase computational cost and the risk of overfitting. In deep learning design, the balance between width and depth is a critical architectural decision affecting not only performance but also training stability.