Going further with model verification and deep learning

In our recent review paper aiming to introduce deep learning to ecologists, we presented a workflow describing the steps required to create a deep learning model. This figure did not present some of the following steps of model use such as model verification. By ensuring model adequacy, model verification is an important step after model creation in order to answer ecological questions. Adding model verification to a deep learning model development workflow can raise some new issues such as detecting the difference among the multiple datasets or what to do when model verification fails. In the spirit of our previous review, we identify some questions users trying to verify their deep learning model can have and try to find, for each, a solution to help them navigate the steps of deep learning model testing. We provide an additional cheat sheet to quickly help answer common questions regarding using model verification and deep learning. We hope these resources help stimulate further synthesis and coherence in the use of deep learning models in ecology.