  Machine learning is a modern approach to problem-solving and task automation.
In particular, machine learning is concerned with the development and
applications of algorithms that can recognize patterns in data and use them for
predictive modeling. Artificial neural networks are a particular class of
machine learning algorithms and models that evolved into what is now described
as deep learning. Given the computational advances made in the last decade,
deep learning can now be applied to massive data sets and in innumerable
contexts. Therefore, deep learning has become its own subfield of machine
learning. In the context of biological research, it has been increasingly used
to derive novel insights from high-dimensional biological data. To make the
biological applications of deep learning more accessible to scientists who have
some experience with machine learning, we solicited input from a community of
researchers with varied biological and deep learning interests. These
individuals collaboratively contributed to this manuscript's writing using the
GitHub version control platform and the Manubot manuscript generation toolset.
The goal was to articulate a practical, accessible, and concise set of
guidelines and suggestions to follow when using deep learning. In the course of
our discussions, several themes became clear: the importance of understanding
and applying machine learning fundamentals as a baseline for utilizing deep
learning, the necessity for extensive model comparisons with careful
evaluation, and the need for critical thought in interpreting results generated
by deep learning, among others.
