  Modern wide field radio surveys typically detect millions of objects.
Techniques based on machine learning are proving to be useful for classifying
large numbers of objects. The self-organizing map (SOM) is an unsupervised
machine learning algorithm that projects a many-dimensional dataset onto a two-
or three-dimensional lattice of neurons. This dimensionality reduction allows
the user to visualize common features of the data better and develop algorithms
for classifying objects that are not otherwise possible with large datasets. To
this aim, we use the PINK implementation of a SOM. PINK incorporates rotation
and flipping invariance so that the SOM algorithm may be applied to
astronomical images. In this cookbook we provide instructions for working with
PINK, including preprocessing the input images, training the model, and
offering lessons learned through experimentation. The problem of imbalanced
classes can be improved by careful selection of the training sample and
increasing the number of neurons in the SOM (chosen by the user). Because PINK
is not scale-invariant, structure can be smeared in the neurons. This can also
be improved by increasing the number of neurons in the SOM. We also introduce
pyink, a Python package used to read and write PINK binary files, assist in
common preprocessing operations, perform standard analyses, visualize the SOM
and preprocessed images, and create image-based annotations using a graphical
interface. A tutorial is also provided to guide the user through the entire
process. We present an application of PINK to VLA Sky Survey (VLASS) images. We
demonstrate that the PINK is generally able to group VLASS sources with similar
morphology together. We use the results of PINK to estimate the probability
that a given source in the VLASS QuickLook Catalogue is actually due to
sidelobe contamination.
