Artificially expanding the datasets through rotation of images in MNIST I came across this question from the 3rd chapter of the book Neural Networks and Deep Learning by Michael Nielsen, this is a question given in his exercise.


  One way of expanding the MNIST training data is to use small rotations
  of training images. What's a problem that might occur if we allow
  arbitrarily large rotations of training images?


I would happy if someone explain why large rotations would be problematic.
The problem is that numbers are not invariant to rotations. 

For example, see what happens when you rotate a 4 in steps of 90 degrees:



So unless your task includes recognizing numbers which are written sideways or upside-down this does not provide a proper data augmentation.