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How does a user's prior experience with deep learning impact accuracy? Wepresent an initial study based on 31 participants with different levels ofexperience. Their task is to perform hyperparameter optimization for a givendeep learning architecture. The results show a strong positive correlationbetween the participant's experience and the final performance. Theyadditionally indicate that an experienced participant finds better solutionsusing fewer resources on average. The data suggests furthermore thatparticipants with no prior experience follow random strategies in their pursuitof optimal hyperparameters. Our study investigates the subjective human factorin comparisons of state of the art results and scientific reproducibility indeep learning.

Black Magic in Deep Learning: How Human Skill Impacts Network Training