diff --git a/neural_network/activation_functions/swish.py b/neural_network/activation_functions/swish.py
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+++ b/neural_network/activation_functions/swish.py
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+"""
+Swish Activation Function
+
+Use Case: Enhances the expression of input data and weight to be learnt.
+For more detailed information, you can refer to the following link:
+https://en.wikipedia.org/wiki/Swish_function
+"""
+
+import numpy as np
+
+
+def swish(vector: np.ndarray) -> np.ndarray:
+    """
+        Implements the Swish activation function.
+
+        Parameters:
+            vector (np.ndarray): The input array for Swish activation.
+
+        Returns:
+            np.ndarray: The output array after applying the Swish activation.
+
+        Formula: f(x) = x / (1 + np.exp(-x))
+
+    Examples:
+    >>> swish(vector=np.array([2.3,0.6,-2,-3.8]))
+    array([ 2.09041719,  0.38739378, -0.23840584, -0.08314883])
+
+    >>> swish(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()