Training neural nets: is it important that the data is randomly sorted?
Yes it is important that the data is randomly ordered. You want to remove patterns of how you descent along the cost function because such patterns can increase the likelihood of overfitting. It's easiest to explain on A,A,A, ... B,B,B ... pattern. Here the network is given an opportunity to adapt to peculiarities of the first training example before moving further. And the goal of training is not to adapt to peculiarities but to find features that generalize well. So it seems that a better idea is to expose the network to a different example in each iteration.

Most research works that I've seen simply randomize the order of examples in each epoch. But it seems sensible to think that a useful pattern might in fact exist. Perhaps one that focuses on maximizing the variety of features that the network is exposed to with each subsequent iteration.