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The rectified linear unit (ReLU) is one of the most common activation functions in machine learning models. As a component of an artificial neuron in artificial neural networks (ANN), the activation function is responsible for processing weighted inputs and helping to deliver an output.
With the ReLU as the activation function, the function returns positive values, but does not return negative values, returning zero if negative input applies.
Experts explain how ReLU works on interaction effects with input and accounts for non-linear results.
In general, ReLU works with principles like gradient descent to supply a model for a working activation function in a neural network. In addition, engineers refine the algorithmic work of machine learning programs and develop layers of neurons in ANNs to help to converge or resolve specific problems tackled by the technologies.
Alternatives to ReLU include sigmoid and tanh functions.