I'm using Python as my programming language here, partly because it's my preferred coding language, but also because there is an embarrassment of options for machine learning packages in Python. And, it's a language that makes it easy to experiment and get things done quickly.
The two machine learning packages I considered for this were scikit-learn and Tensorflow. scikit-learn has a great breadth of machine learning algorithms; Tensorflow is focused on neural networks and has a lot of depth there.
I'm starting with scikit-learn mostly because it was easier to get started with, and to swap in and out different types of (for example) classifier algorithms with a consistent API for fitting and predicting. Plus, I'm expecting to use machine learning techniques other than neural networks for some of the stages in the processing of a board image, which makes Tensorflow less appropriate.
Of course, if it ends up making sense, I can use Tensorflow in places and scikit-learn in others, but that makes the code harder to maintain later, so I'll try to stick with one ML package if I can.
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