That's right. In fact, for a subclass of image recognition tasks, MNIST has become the standard benchmark. Pretty much all of Geoff Hinton's early work on deep

That's right. In fact, for a subclass of image recognition tasks, MNIST has become the standard benchmark. Pretty much all of Geoff Hinton's early work on deep learning used MNIST to track the progress of his (their) methods. In fact, if the goal of the book is to educate people on the field then I would say it's definitely best to use the standard benchmarks. It lets readers relate what's in the book to the literature, should they desire, and just like in academia, it lends credence to the author's statements. I've seen people take a lot of flak for publishing writings that don't use the standard datasets, but make claims of progress.