Hidden layer

Example of hidden layer in a deep neural network

In artificial neural networks (ANN), the hidden layer is layer of artificial neurons that may be applied. It is neither the input nor the output layer, and is positioned between both. An example of an ANN utilizing a hidden layer is the feedforward neural network.

The hidden layers transform inputs from the input layer to the output layer. This is accomplished by applying what are called weights to the inputs and passing them through what is called an activation function, which calculate input based on input and weight. This allows the artificial neural network to learn non-linear relationships between the input and output data.

Each neuron in a hidden layer receives inputs from all the neurons in the previous layer, multiplies these inputs by its weights, adds a bias term, and then passes the result through an activation function. The output of each neuron is then used as input to the next layer.

The weighted inputs can be randomly assigned. They can also be fine-tuned and calibrated through what is called backpropagation.