Which of the following statements about stochastic gradient descent is not true?

Group of answer choices

One of the advantages of stochastic gradient descent is that it can start progress in improving the parameters $\backslash$theta after looking at just a single training example; in contrast, batch gradient descent needs to take a pass over the entire training set before it starts to make progress in improving the parameters$$ values.

In each iteration of stochastic gradient descent, the algorithm needs to examine$/$use only one training example.

Before running stochastic gradient descent, you should randomly shuffle $($reorder$)$ the training set.

If you have a huge training set, then stochastic gradient descent may be much faster than batch gradient descent.

The cost functionis guaranteed to decrease after every iteration of the stochastic gradient descent algorithm.