diff --git a/computer_vision/pooling_functions.py b/computer_vision/pooling_functions.py
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+# Source : https://computersciencewiki.org/index.php/Max-pooling_/_Pooling
+# Importing the libraries
+import numpy as np
+from PIL import Image
+
+
+# Maxpooling Function
+def maxpooling(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:
+        arr: numpy array
+        size: size of pooling matrix
+        stride: the number of pixels shifts over the input matrix
+    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)
+    array([[ 6.,  8.],
+           [14., 16.]])
+    >>> maxpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
+    array([[241., 180.],
+           [241., 157.]])
+    """
+    arr = np.array(arr)
+    if arr.shape[0] != arr.shape[1]:
+        raise ValueError("The input array is not a square matrix")
+    i = 0
+    j = 0
+    mat_i = 0
+    mat_j = 0
+
+    # compute the shape of the output matrix
+    maxpool_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))
+
+    while i < arr.shape[0]:
+        if i + size > arr.shape[0]:
+            # if the end of the matrix is reached, break
+            break
+        while j < arr.shape[1]:
+            # if the end of the matrix is reached, break
+            if j + size > arr.shape[1]:
+                break
+            # compute the maximum of the pooling matrix
+            updated_arr[mat_i][mat_j] = np.max(arr[i : i + size, j : j + size])
+            # shift the pooling matrix by stride of column pixels
+            j += stride
+            mat_j += 1
+
+        # shift the pooling matrix by stride of row pixels
+        i += stride
+        mat_i += 1
+
+        # reset the column index to 0
+        j = 0
+        mat_j = 0
+
+    return updated_arr
+
+
+# Averagepooling Function
+def avgpooling(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:
+        arr: numpy array
+        size: size of pooling matrix
+        stride: the number of pixels shifts over the input matrix
+    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)
+    array([[ 3.,  5.],
+           [11., 13.]])
+    >>> avgpooling([[147, 180, 122],[241, 76, 32],[126, 13, 157]], 2, 1)
+    array([[161., 102.],
+           [114.,  69.]])
+    """
+    arr = np.array(arr)
+    if arr.shape[0] != arr.shape[1]:
+        raise ValueError("The input array is not a square matrix")
+    i = 0
+    j = 0
+    mat_i = 0
+    mat_j = 0
+
+    # compute the shape of the output matrix
+    avgpool_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))
+
+    while i < arr.shape[0]:
+        # if the end of the matrix is reached, break
+        if i + size > arr.shape[0]:
+            break
+        while j < arr.shape[1]:
+            # if the end of the matrix is reached, break
+            if j + size > arr.shape[1]:
+                break
+            # compute the average of the pooling matrix
+            updated_arr[mat_i][mat_j] = int(np.average(arr[i : i + size, j : j + size]))
+            # shift the pooling matrix by stride of column pixels
+            j += stride
+            mat_j += 1
+
+        # shift the pooling matrix by stride of row pixels
+        i += stride
+        mat_i += 1
+        # reset the column index to 0
+        j = 0
+        mat_j = 0
+
+    return updated_arr
+
+
+# Main Function
+if __name__ == "__main__":
+    from doctest import testmod
+
+    testmod(name="avgpooling", verbose=True)
+
+    # Loading the image
+    image = Image.open("path_to_image")
+
+    # Converting the image to numpy array and maxpooling, displaying the result
+    # Ensure that the image is a square matrix
+
+    Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()
+
+    # Converting the image to numpy array and averagepooling, displaying the result
+    # Ensure that the image is a square matrix
+
+    Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()