Setting hyper-parameters in Deep learning models is considered more of intuitive or some form of black art.
Hyper-Parameter Optimization (HPO) methods paves a principled approach of finding it.
Apart from providing a principled approach, why HPO should be considered seriously? In practice, does HPO makes any significant difference and how it offsets the time that has been invested in doing HPO?
Are their any real-life examples where HPO played a significant role in improving the Deep Learning models?