2.8 Which of the following points hold true for gradient descent?

It is an iterative algorithm and every step it finds the gradient of the cost function with

respect to the parameters to minimize the cost.

For a pure convex function or a regular bowl-shaped cost function, it can converge to

the optimal solution irrespective of the learning rate

Since cost functions can be of any shape, it can only achieve local minima but not global

minimum, provided the cost function is not convex.

Normalizing the variables and bringing the magnitude of these variables to same scale,

ensures faster convergence.