  Deep learning methods are increasingly being applied to problems in medicine
and healthcare. However, few epidemiologists have received formal training in
these methods. To bridge this gap, this article introduces to the fundamentals
of deep learning from an epidemiological perspective. Specifically, this
article reviews core concepts in machine learning (overfitting, regularization,
hyperparameters), explains several fundamental deep learning architectures
(convolutional neural networks, recurrent neural networks), and summarizes
training, evaluation, and deployment of models. We aim to enable the reader to
engage with and critically evaluate medical applications of deep learning,
facilitating a dialogue between computer scientists and epidemiologists that
will improve the safety and efficacy of applications of this technology.
