  This paper presents a concept of a novel method for adjusting
hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer
monitors a performance of a selected Deep Learning algorithm. The observer
learns to model the DL algorithm using a series of random experiments.
Consequently, it may be used for predicting a response of the DL algorithm in
terms of a selected quality measurement to a set of hyper-parameters. This
allows to construct an ensemble composed of a series of evaluators which
constitute an observer-assisted architecture. The architecture may be used to
gradually iterate towards to the best achievable quality score in tiny steps
governed by a unit of progress. The algorithm is stopped when the maximum
number of steps is reached or no further progress is made.
