A hidden layer in an artificial neural network is a layer in between input layers and output layers, where artificial neurons take in a set of weighted inputs and produce an output through an activation function. It is a typical part of nearly any neural network in which engineers simulate the types of activity that go on in the human brain.
Hidden neural network layers are set up in many different ways. In some cases, weighted inputs are randomly assigned. In other cases, they are fine-tuned and calibrated through a process called backpropagation. Either way, the artificial neuron in the hidden layer works like a biological neuron in the brain – it takes in its probabilistic input signals, works on them and converts them into an output corresponding to the biological neuron’s axon.
Many analyses of machine learning models focus on the construction of hidden layers in the neural network. There are different ways to set up these hidden layers to generate various results – for instance, convolutional neural networks that focus on image processing, recurrent neural networks that contain an element of memory and simple feedforward neural networks that work in a straightforward way on training data sets.
