Which of these statements about mini$-$batch gradient descent do you agree with?

a$.$

Training one epoch $($one pass through the training set$)$ using mini$-$batch gradient descent is faster than training one epoch using batch gradient descent.

b$.$

One iteration of mini$-$batch gradient descent $($computing on a single mini$-$batch$)$ is faster than one iteration of batch gradient descent.

c$.$

If the mini$-$batch size is m$,$ you end up with stochastic gradient descent, which has to process the whole training set before making progress.