Neural networks consist of interconnected layers of nodes, or neurons, which process input data to produce an output. The architecture typically includes an input layer, one or more hidden layers, and an output layer. Each connection between neurons has an associated weight that is adjusted during training to minimize the error between the predicted and actual outcomes. This process is known as backpropagation.
