Dealing with class imbalance in Deep Learning

**Have you checked the [list of proposed tips](https://github.com/Benjamin-Lee/deep-rules/issues?q=is%3Aissue+is%3Aopen+label%3Atip) to see if the tip has already been proposed?**

- [x] Yes

**Did you add yourself as a [contributor](https://github.com/Benjamin-Lee/deep-rules/blob/master/contributors.md) by making a pull request if this is your first contribution?**

- [ ] Yes, I added myself or am already a contributor

**Feel free to elaborate, rant, and/or ramble.**
There might be a imbalance in the class distribution, which is quite common in Bioinformatics problems. I believe most of the points regarding dealing with imbalance in ML should work in Deep Learning as well-

1) Try rephrasing the problem
2) Obtain more data
3) Tweak weights appropriately for class imbalance
4) Applying Regularization techniques
5) Use Oversampling or Undersampling techniques(?)
6) Using K-fold CV in the correct way

**Any citations for the rule?** (peer-reviewed literature preferred but not required)
- [https://doi.org/10.1186/s40537-018-0151-6](https://doi.org/10.1186/s40537-018-0151-6)
- [https://doi.org/10.1109/TKDE.2008.239](https://doi.org/10.1109/TKDE.2008.239)

No source file or test is named. Start by reviewing the proposed tips list and the cited papers, then define one actionable, non-overlapping rule for handling class imbalance in deep learning. Done means the project has an agreed tip supported by appropriate evidence.

Written by the indexing model from the issue text.