Estimating a minimum required sample size is one of the most common questions asked by clinical or biomedical collaborators before embarking on a research project. This is especially true when ML is an option. This paper provides rules of thumb and a digestible amount of theory that could inform such conversations, and will surely become a popular reference.

Note intuition from traditional statistics does not universally apply to deep learning and/or extremely high-dimensional data. For example, deep neural networks with 1-4 orders of magnitude more parameters than training examples can still generalize well to unseen data.