input: Write a Python code to implement Gradient Descent Algorithm.
  
  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
  
  assert gradient_descent(np.array([1, 2, 3, 4, 5]), np.array([5, 7, 9, 11, 13]), 0.01, 100) == (2.0000000000000018, 3.0000000000000027)
  assert gradient_descent(np.array([1, 2, 3, 4, 5]), np.array([5, 7, 9, 11, 13]), 0.1, 100) == (2.0000000000000018, 2.999999999999997)
  assert gradient_descent(np.array([1, 2, 3, 4, 5]), np.array([5, 7, 9, 11, 13]), 0.001, 100) == (2.0000000000000018, 3.0000000000000027)
  assert gradient_descent(np.array([]), np.array([]), 0.01, 100) == (0, 0)
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
  