Dynamic Stopping for Artificial Neural Networks
The growing popularity of Artificial Neural Networks (ANN) demands continuous improvement and optimization of the training process to achieve higher-performing algorithms at a lower computational cost. During training an ANN learns to solve a problem by looking at examples, and will iteratively go over a dataset to reach an optimal performance. Usually the user needs to define a fixed number of it
