During the public Kaggle competition "IceCube -- Neutrinos in Deep Ice",
thousands of reconstruction algorithms were created and submitted, aiming to
estimate the direction of neutrino events recorded by the IceCube detector.
Here we describe in detail the three ultimate best, award-winning solutions.
The data handling, architecture, and training process of each of these machine
learning models is laid out, followed up by an in-depth comparison of the
performance on the kaggle datatset. We show that on cascade events in IceCube
above 10 TeV, the best kaggle solution is able to achieve an angular resolution
of better than 5 degrees, and for tracks correspondingly better than 0.5
degrees. These performance measures compare favourably to the current
state-of-the-art in the field