<jupyter_start><jupyter_text>Using Python Codes, to find the inverse of matrix, the matrix is passed to the linalg. inv() of the NumPy module:<jupyter_code>import numpy as np
A=np.array([[4,3],[-5,9]])
print(A)
import numpy as np
A=np.array([[4,3],[-5,9]])
print(A)
inv_A = np.linalg.inv(A)
print(inv_A)
B = np.array([[20],[26]])
print(B)
X = np.linalg.inv(A).dot(B)
print(X)


X = np.dot(inv_A,B)
print(X)

A = np.array ([[20,10],[17,22]])
print (A)
inv_A  = np.linalg.inv(A)
print (inv_A)
B = np.array([[350],[500]])
print(B)
X = np.dot(inv_A,B)
print(X)

B=np.dot(A,X)
print (B)<jupyter_output>[[350.]
 [500.]]<jupyter_text>ACTIVITY<jupyter_code>import numpy as np
A = np.array([[[4,3,2],[-2,2,3],[3,-5,2]]]) #creation of matrix A
print (A)
inv_A = np.linalg.inv(A)
print(inv_A)
B = np.array ([[25],[-10],[-4]])
print(B)
X = np.linalg.inv(A).dot(B)
print (X)
X = np.dot(inv_A,B)
print(X)<jupyter_output>[[[ 5.]
  [ 3.]
  [-2.]]]
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