  There is a parameter ubiquitous throughout the deep learning world: learning
rate. There is likewise a ubiquitous question: what should that learning rate
be? The true answer to this question is often tedious and time consuming to
obtain, and a great deal of arcane knowledge has accumulated in recent years
over how to pick and modify learning rates to achieve optimal training
performance. Moreover, the long hours spent carefully crafting the perfect
learning rate can come to nothing the moment your network architecture,
optimizer, dataset, or initial conditions change ever so slightly. But it need
not be this way. We propose a new answer to the great learning rate question:
the Autonomous Learning Rate Controller. Find it at
https://github.com/fastestimator/ARC/tree/v2.0
