Hidden Layer

What Does Hidden Layer Mean?

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.

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Techopedia Explains Hidden Layer

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.

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Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.