[curuinor]

Use dumber ML first, try some random forests. Not even because they're even that much better or worse, just because DL requires an enormous amount of knowledge and fiddliness but what you prolly want is for the bulk of the actual work to set up the data for ML, not hyperparameter fiddling and architecture fiddling.

[shoyer]

Before you try any sort of ML, explore your data [1, 2]. Exploratory data analysis may very well tell you that there is absolutely no point in making a fancy predictive model at all. If a few heuristics get you 90% of the way to an optimal solution, then don't even bother to start on machine learning, unless that last 10% is going to provide significant value.

[1] https://en.wikipedia.org/wiki/Exploratory_data_analysis

[2] From one of my mentors: http://www.unofficialgoogledatascience.com/2016/10/practical...

[cr0sh]

I agree with this - try the "simpler" solutions first to see if they'll model what you need. No sense in getting lost in more complex methods if a simpler solution will suffice.