  Stochastic Gradient Descent (SGD) methods are prominent for training machine
learning and deep learning models. The performance of these techniques depends
on their hyperparameter tuning over time and varies for different models and
problems. Manual adjustment of hyperparameters is very costly and
time-consuming, and even if done correctly, it lacks theoretical justification
which inevitably leads to "rule of thumb" settings. In this paper, we propose a
generic approach that utilizes the statistics of an unbiased gradient estimator
to automatically and simultaneously adjust two paramount hyperparameters: the
learning rate and momentum. We deploy the proposed general technique for
various SGD methods to train Convolutional Neural Networks (CNN's). The results
match the performance of the best settings obtained through an exhaustive
search and therefore, removes the need for a tedious manual tuning.
