diff --git a/neural_network/activation_functions/leaky_rectified_linear_unit.py b/neural_network/activation_functions/leaky_rectified_linear_unit.py
new file mode 100644
index 000000000..1e99fed4e
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+++ b/neural_network/activation_functions/leaky_rectified_linear_unit.py
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+"""
+Leaky Rectified Linear Unit (LeakyReLU)
+
+Input: vector (type: np.ndarray) , alpha (type: float)
+Output: vector (type: np.ndarray)
+
+UseCase: LeakyReLU solves the issue of dead neurons or vanishing gradient problem.
+Refer the below link for more information:
+https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Leaky_ReLU
+
+Applications:
+Generative Adversarial Networks (GANs)
+Object Detection and Image Segmentation
+"""
+
+import numpy as np
+
+
+def leaky_rectified_linear_unit(vector: np.ndarray, alpha: float) -> np.ndarray:
+    """
+         Implements the LeakyReLU activation function.
+         Parameters:
+             vector: the array containing input of elu activation
+             alpha: hyperparameter
+         return:
+         leaky_relu (np.array): The input numpy array after applying leaky-relu.
+
+         Formula : f(x) = x if x > 0 else f(x) = alpha * x
+
+    Examples:
+    >>> leaky_rectified_linear_unit(vector=np.array([2.3,0.6,-2,-3.8]), alpha=0.3)
+    array([ 2.3 ,  0.6 , -0.6 , -1.14])
+
+    >>> leaky_rectified_linear_unit(vector=np.array([-9.2,-0.3,0.45,-4.56]), \
+    alpha=0.067)
+    array([-0.6164 , -0.0201 ,  0.45   , -0.30552])
+
+    """
+    return np.where(vector > 0, vector, alpha * vector)
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()