Make sure your problem is a good fit for deep learning.

Not all problems are equally amenable to deep learning. Make sure that there is a high prior probability that your problem will benefit from deep learning. Some useful rules of thumb:

- Is there "structure" in your data. Data types such as imaging, text, time series, etc contain useful and regular kinds of structure and correlation that a deep learning model can more easily exploit relative to traditional models. If all you have is tabular data then DL is unlikely to provide much lift.

- Do you have a lot of data? Deep learning is much more scalable (due to the ability to leverage GPU computing) and can take advantage of large datasets more easily.

- Do you want to assess statistical significance? If so deep learning might not be the best fit.

Start by reviewing the guidance in the issue body and the surrounding manuscript content to determine where this rule of thumb belongs. Done means the deep-learning fit guidance is incorporated into the appropriate documentation section with its points about data structure, dataset size, and statistical significance preserved.

Written by the indexing model from the issue text.