When Not to Use Deep Learning

There is also an aspect of deep learning models that I see gets sort of lost in translation when coming from other fields of machine learning. Most tutorials and introductory material to deep learning describe these models as composed by hierarchically-connected layers of nodes where the first layer is the input and the last layer is the output and that you can train them using some form of stochastic gradient descent. After maybe some brief mentions on how stochastic gradient descent works and what backpropagation is, the bulk of the explanation focuses on the rich landscape of neural network types (convolutional, recurrent, etc.). The optimization methods themselves receive little additional attention, which is unfortunate since it's likely that a big (if not the biggest) part of why deep learning works is because of those particular methods (check out, e.g. this post from Ferenc Huszár's and this paper taken from that post), and knowing how to optimize their parameters and how to partition data to use them effectively is crucial to get good convergence in a reasonable amount of time. Exactly why stochastic gradients matter so much is still unknown, but some clues are emerging here and there.