I've had to build some relatively simple deep learning systems around tensorflow, and I don't have any special training other than the usual engineering math an

I've had to build some relatively simple deep learning systems around tensorflow, and I don't have any special training other than the usual engineering math and statistics. My observation is that it's much more important to be clever with identifying possible inputs to train on rather than focusing too much on the machine learning itself. A crappy ML implementation that was trained on 20 data sets which are highly relevant and well curated does better than a highly tuned ML system with half the inputs and bad outliers.