There are different levels of stochasticity in machine learning. Sometimes they're in the process of sampling the dataset, and other times in the machine learning models (specifically neural networks) themselves. While stochasticity brings a number of advantages in model training, it also introduces some gnarly problems with reproducibility.
In this report, we'll go over some of the methods that promise to make our machine learning experiments more reproducible. Before we jump to the nitty-gritty of that, we would discuss some motivation behind ensuring our machine learning experimentation is reproducible.
Let's get started!