Rectified Linear Unit (ReLU) Function in Machine Learning

Guide to Rectified Linear Unit Activation Function

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TL;DR

Below is a quick summary of Rectified Linear Unit activation function:

• What is the rectified linear unit (ReLU) and why is it used?

The rectified linear unit is a piecewise linear function defined as f(x) = max(0, x). It is the most popular activation function because it is computationally efficient, avoids the vanishing gradient problem, and enables sparse activation in deep neural networks.

• How does the ReLU activation function handle negative values?

The ReLU function outputs zero for all negative inputs and returns the input value directly for all positive inputs. This thresholding at zero helps deep learning models focus on relevant features by shutting down neurons with negative values.

• What is the dying ReLU problem in machine learning?

The dying ReLU problem occurs when a neuron permanently outputs zero because it receives large negative inputs, leading to a zero gradient. This prevents the learning process for that neuron, causing it to die and stop contributing to the training process.

• How does ReLU compare to sigmoid or tanh functions?