Neural network when not to shuffle? So for the MLP regressor in sklearn there is the option to shuffle. And I assume it means that before dividing into batches the data is shuffled and thus at each iteration the batches are different from the batches in the previous iteration?
Now, I did read about why to shuffle but are there also instances when not to shuffle? I thought probably with time-series data? Because you destroy the temporal ordering?

Shuffling does not harm. It helps with the gradient updates. For time series data, we prepare the data such that the entries of previous time instants are fed into RNNs in order, i.e. $X, y$, where $X$ contains the previous time instants' targets or features. Shuffling is applied for different data samples, i.e. $(X_1,y_1), (X_2,y_2) ...$
Each libraries' shuffling logic can be different. In MLPRegressor, it says

Whether to shuffle samples in each iteration

So, in each iteration (probably epoch), the data is shuffled. So, at every epoch, the batches will be different.
