ReLU

ReLU (Rectified Linear Unit) is the most widely used activation function in modern deep learning. Its definition is dead simple — output zero for negative inputs and the input itself for positive values: f(x) = max(0, x). Despite this simplicity, ReLU was a major breakthrough because it avoided the vanishing-gradient problem of sigmoid and tanh activations, allowing networks to be trained much deeper. ReLU was popularized around 2010 and quickly became the default activation in convolutional networks like AlexNet and ResNet. Variants include Leaky ReLU (allows small negative slope), Parametric ReLU (learns the slope), GELU (smoothed version used in transformers), and SwiGLU (a gated variant used in Llama). PyTorch and TensorFlow both expose ReLU as a built-in layer. AI governance teams encounter this AI term in model cards and AI compliance documentation produced for responsible AI programs, particularly when reviewing model architectures during AI risk management evaluations.

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