- Be prepared. In my case the material was sent out a week beforehand. Use the time to setup your machine(s) and code base so you can start directly.
- Do research on the topic. I’ve not looked into state of the art algorithms for the problem beforehand. This cost me a lot of time in the Hackathon, to understand the algorithm we used.
- Have compute resources ready. I could have used 2 further machines, but forgot to setup ssh, Teamviewer or any other remote control on them.
- Do whatever you know best. Some of my team members had far more experience on the topic than me. I’ve tried to follow there example, which wasn’t successful. I think I would have been more successful using more basic approaches (which I have enough knowledge about) than trying to follow the “state of the art” approach. At the end I’ve tried to support them as best as I could (mostly doing evaluations).
- Push for more organization. We quickly came to the conclusion that only 1 or 2 approaches (transfer learning on common models) would be feasible in the short time of a hackathon. This led to us being rather unorganized. Everybody tried to get the models running as fast as possible and tweaking them the rest of the time. I think we could have produced more insight on the topic with regular “stand ups”.
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