diff --git a/neural_network/activation_functions/sigmoid_linear_unit.py b/neural_network/activation_functions/sigmoid_linear_unit.py
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index 000000000..f69eb1159
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+++ b/neural_network/activation_functions/sigmoid_linear_unit.py
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+"""
+Implements the Sigmoid Linear Unit or SiLU function
+also known as Swiss functions.
+
+The function takes a vector of K real numbers and then
+applies the SiLU function to each element of the vector.
+
+Script inspired from its corresponding Wikipedia article
+https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
+https://en.wikipedia.org/wiki/Swish_function
+"""
+
+import numpy as np
+
+
+def sigmoid_linear_unit(vector: np.ndarray) -> np.ndarray:
+    """
+         Implements the SiLU activation function.
+         Parameters:
+             vector: the array containing input of SiLU activation
+         return:
+            The input numpy array after applying SiLU.
+
+         Mathematically, f(x) = x *  1/1+e^(-x)
+
+    Examples:
+    >>> sigmoid_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]))
+    array([ 2.09041719,  0.38739378, -0.23840584, -0.08314883])
+
+    >>> sigmoid_linear_unit(vector=np.array([-9.2,-0.3,0.45,-4.56]))
+    array([-0.00092947, -0.12766724,  0.27478766, -0.04721304])
+
+    """
+    return vector / (1 + np.exp(-vector))
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()