Great question — answering as the UphillTech team:
This is one of the most difficult and unsolved problems in machine learning and deep learning! There are many different ways to estimate the most appropriate hyper-parameters, such as grid search, random search, Bayesian optimization, meta-learning, reinforcement learning, and evolutionary algorithms (e.g. NEAT). However, the problem is that most if not all these approaches are typically not very computationally efficient if your model isn't anything but very small. The number of possible configurations of the hyper-parameters is very large. If you don't have good computational resources (e.g. GPUs and powerful servers), you probably out of luck or require some days to get some insight. In...
Full answer by nbro: https://ai.stackexchange.com/a/20603 (CC BY-SA 4.0).
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