SwiGLU Activation Function
SwiGLU (Swish-Gated Linear Unit) is a novel activation function that combines the advantages of the Swish activation function and the Gated Linear Unit (GLU). This activation function was proposed in a paper by researchers at the University of Copenhagen in 2019, and has since gained popularity in the deep learning community.
In this blog post, we will explore the SwiGLU activation function in detail and discuss its advantages over other activation functions. this post was Originally published by AI-ContentLab
What is an Activation Function?
In neural networks, activation functions are used to introduce non-linearity into the output of a neuron. They are responsible for deciding whether or not a neuron should be activated, based on the input it receives.
Activation functions help neural networks to learn complex non-linear relationships between inputs and outputs. There are several types of activation functions used in deep learning, such as the ‘sigmoid’, ‘ReLU’, and ‘tanh’.
Left: GeLU, Right: Swish
(Source: https://towardsdatascience.com/on-the-disparity-between-swish-and-gelu-1ddde902d64b)