Inputs pass through the layers to produce outputs, and training adjusts weights to reduce a loss. Architecture, data, objective, and optimization determine which patterns the network learns.
A neural network is a parameterized function built from connected layers of weighted transformations and nonlinear activations.
Inputs pass through the layers to produce outputs, and training adjusts weights to reduce a loss. Architecture, data, objective, and optimization determine which patterns the network learns.