[Discussion] How to obtain meaningful conclusions from deep learning experiments to decide for the use of different parameters and architectures?

Web: https://www.reddit.com/r/MachineLearning/comments/shg9bx/discussion_how_to_obtain_meaningful_conclusions/

As I wrote already on reddit before, I am working on my master thesis degree, which is about generating synthetic data for jersey number recognition. But I have been puzzled since the beggining by how to determine experimentally in a good manner which parameters to use? When generating synthetic data, there is a ton of parameters and techniques that define the distribution of the resulting data and I simply don't know what is an acceptable methodology to choose such parameters …

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