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Why do Transformer models like BERT use GELU instead of the traditional ReLU activation function? The answer lies in how these functions process information. While ReLU completely removes negative inputs, GELU uses a smooth, input-dependent mechanism to decide how much information should pass through. This small mathematical difference can influence how neural networks learn complex patterns.
In this article, we will understand the GELU activation function, explore its mathematics, compare it with ReLU, and implement both functions using Python.
GELU stands for Gaussian Error Linear Unit. It is a nonlinear activation function introduced by Dan Hendrycks and Kevin Gimpel in 2016.
Source: Analytics Vidhya — Read the full article on the original site ↗