diff --git a/computer_vision/pooling_functions.py b/computer_vision/pooling_functions.py
index 09beabcba82d..dac9d84b4ed8 100644
--- a/computer_vision/pooling_functions.py
+++ b/computer_vision/pooling_functions.py
@@ -1,11 +1,16 @@
-# Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
-# Importing the libraries
+"""
+Sources :
+ Max Pooling: https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
+ Average Pooling: https://computersciencewiki.org/index.php/Average_pooling
+ Global Average Pooling: https://paperswithcode.com/method/global-average-pooling
+"""
+
 import numpy as np
 from PIL import Image
 
 
-# Maxpooling Function
-def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
+# Max pooling Function
+def max_pooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     """
     This function is used to perform maxpooling on the input array of 2D matrix(image)
     Args:
@@ -15,10 +20,10 @@ def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     Returns:
         numpy array of maxpooled matrix
     Sample Input Output:
-    >>> maxpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
+    >>> max_pooling(np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]), 2, 2)
     array([[ 6.,  8.],
            [14., 16.]])
-    >>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
+    >>> max_pooling(np.array([[147, 180, 122],[241, 76, 32],[126, 13, 157]]), 2, 1)
     array([[241., 180.],
            [241., 157.]])
     """
@@ -31,9 +36,9 @@ def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     mat_j = 0
 
     # compute the shape of the output matrix
-    maxpool_shape = (arr.shape[0] - size) // stride + 1
+    max_pool_shape = (arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape maxpool_shape
-    updated_arr = np.zeros((maxpool_shape, maxpool_shape))
+    updated_arr = np.zeros((max_pool_shape, max_pool_shape))
 
     while i < arr.shape[0]:
         if i + size > arr.shape[0]:
@@ -60,8 +65,8 @@ def maxpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     return updated_arr
 
 
-# Averagepooling Function
-def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
+# Average pooling Function
+def avg_pooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     """
     This function is used to perform avgpooling on the input array of 2D matrix(image)
     Args:
@@ -71,10 +76,10 @@ def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     Returns:
         numpy array of avgpooled matrix
     Sample Input Output:
-    >>> avgpooling([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]], 2, 2)
+    >>> avg_pooling(np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]]), 2, 2)
     array([[ 3.,  5.],
            [11., 13.]])
-    >>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
+    >>> avg_pooling(np.array([[147, 180, 122],[241, 76, 32],[126, 13, 157]]), 2, 1)
     array([[161., 102.],
            [114.,  69.]])
     """
@@ -87,9 +92,9 @@ def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     mat_j = 0
 
     # compute the shape of the output matrix
-    avgpool_shape = (arr.shape[0] - size) // stride + 1
+    avg_pool_shape = (arr.shape[0] - size) // stride + 1
     # initialize the output matrix with zeros of shape avgpool_shape
-    updated_arr = np.zeros((avgpool_shape, avgpool_shape))
+    updated_arr = np.zeros((avg_pool_shape, avg_pool_shape))
 
     while i < arr.shape[0]:
         # if the end of the matrix is reached, break
@@ -115,21 +120,52 @@ def avgpooling(arr: np.ndarray, size: int, stride: int) -> np.ndarray:
     return updated_arr
 
 
-# Main Function
+# Global Average pooling Function
+def global_average_pooling(arr: np.ndarray) -> np.ndarray:
+    """
+    This function performs global average pooling on the input 2D matrix (image).
+    Args:
+        arr: numpy array
+            The input 2D matrix.
+    Returns:
+        numpy array of the global average pooled matrix.
+
+    >>> global_average_pooling(np.array([[1,2,3],[5,6,7],[9,10,11],[13,14,15]]))
+    array([[8.]])
+    >>> global_average_pooling(np.array([[147, 180, 122],[241, 76, 32],[126, 13, 157]]))
+    array([[121.55555556]])
+    """
+    arr = np.array(arr)
+
+    # Calculate the average of the entire input matrix
+    average_value = np.mean(arr)
+
+    # Create a matrix with a single element, which is the average value
+    global_avg_pooled_matrix = np.array([[average_value]])
+
+    return global_avg_pooled_matrix
+
+
+# Test with Images
 if __name__ == "__main__":
     from doctest import testmod
 
-    testmod(name="avgpooling", verbose=True)
+    testmod(name="average_pooling", verbose=True)
 
     # Loading the image
-    image = Image.open("path_to_image")
+    image = Image.open("/path_to_image")
+
+    # Converting the image to numpy array and max_pooling, displaying the result
+    # Ensure that the image is a square matrix
+
+    Image.fromarray(max_pooling(np.array(image), size=3, stride=2)).show()
 
-    # Converting the image to numpy array and maxpooling, displaying the result
+    # Converting the image to numpy array and average_pooling, displaying the result
     # Ensure that the image is a square matrix
 
-    Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
+    Image.fromarray(avg_pooling(np.array(image), size=3, stride=2)).show()
 
-    # Converting the image to numpy array and averagepooling, displaying the result
+    # Converting the image to numpy array and average_pooling, displaying the result
     # Ensure that the image is a square matrix
 
-    Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()
+    Image.fromarray(global_average_pooling(np.array(image))).show()