If you don't have enough data, consider whether there is other data that can help pre-train a model.

Often we lack a large amount of labeled data, but sometimes there are abundant unlabeled data. We could touch on pre-training approaches (autoencoder pre-training, etc) as well as transfer + refinement (though you have that in #6 so transfer could just be added there). Perhaps this ends up subsumed into a broader point as people contribute their tips.