input: Write a function to implement Gradient Descent Algorithm in Python. The function should take in inputs x, y, learning_rate, and iterations. It should return the updated values of m_curr and b_curr after performing the specified number of iterations.
  
  
  def gradient_descent(x, y, learning_rate=0.01, iterations=100): 
      m_curr = b_curr = 0
      n = len(x)
      for i in range(iterations):
          y_predicted = m_curr * x + b_curr
          cost = (1/n) * sum([val**2 for val in (y-y_predicted)])
          md = -(2/n)*sum(x*(y-y_predicted))
          bd = -(2/n)*sum(y-y_predicted)
          m_curr = m_curr - learning_rate * md
          b_curr = b_curr - learning_rate * bd
          print ("m {}, b {}, cost {} iteration {}".format(m_curr,b_curr,cost, i))
      return m_curr, b_curr
  
  
  # Test cases
  x = np.array([1, 2, 3, 4, 5])
  y = np.array([5, 7, 9, 11, 13])
  print(gradient_descent(x, y, learning_rate=0.01, iterations=100))
  
  # Expected output
  # (2, 3)
output: def gradient_descent(x, y, learning_rate=0.01, iterations=100): 
      m_curr = b_curr = 0
      n = len(x)
      for i in range(iterations):
          y_predicted = m_curr * x + b_curr
          cost = (1/n) * sum([val**2 for val in (y-y_predicted)])
          md = -(2/n)*sum(x*(y-y_predicted))
          bd = -(2/n)*sum(y-y_predicted)
          m_curr = m_curr - learning_rate * md
          b_curr = b_curr - learning_rate * bd
      return m_curr, b_curr