  Leaping into the rapidly developing world of deep learning is an exciting and
sometimes confusing adventure. All of the advice and tutorials available can be
hard to organize and work through, especially when training specific models on
specific datasets, different from those originally used to train the network.
In this short guide, we aim to walk the reader through the techniques that we
have used to successfully train two deep neural networks for pixel-wise
classification, including some data management and augmentation approaches for
working with image data that may be insufficiently annotated or relatively
homogenous.
