Layer: Difference between revisions

Each layer in a neural network performs computations on data received from the previous layer. A layer's computation can be represented as the dot product of inputs and weights, followed by application of an activation function. The outputs from a layer are then fed back into the next one in the network, with this cycle repeated until an accurate final output is produced. These weights and biases are learned by the network through training, where its parameters are updated in order to minimize loss functions which measure differences between predicted outputs and actual ones.

Neural networks consist of three layers: input layers, hidden layers and output. The input layer is the initial hub in the network and it receives input data before passing it along to the next. Conversely, the output layer produces the final output after processing all previous input data. Finally, hidden layers sit between input and output layers and perform most of the computation for a network.