In deep learning, a network layer is a structure in the model architecture that takes information (weighted input) from the previous layer, processes it, and then passes it (as output) to the next layer. Different layers may perform different transformations on the inputs. Network layers serve as the building blocks in deep learning. Neural networks are composed of, at least, the first layer (the input layer), the last layer (the output layer), and multiple layers in between (hidden layers).
