K-Fold Cross Validation for NNs
It depends.  If you're evaluating your model's performance without tuning hyperparameters, then a train/test split is sufficient.

If you're tuning hyperparameters, then you need a validation set.  Within each fold, you'll train on the training set (of course), using the validation set to tune hyperparameters.  Then you'll evaluate performance on the test set.