Trying to understand when NN/CNN/LSTM/etc... go wrong. • /r/MachineLearning

The achievements of neural nets have been truly astounding. They seem to be setting the bar in terms of performance in many/all ML challenges. I am, however, curious about where they fail. I am trying to understand a sort of meta decision boundary in "problem space" as to which type of algorithm to select for a given problem. I am aware that things like logistic regression are far easier to implement and may perform equally on simple enough tasks.