Abstract as much as possible

**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?**

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

**Feel free to elaborate, rant, and/or ramble.**

I just discovered [Ludwig](https://github.com/uber/ludwig), a DL toolkit which uses YAML configuration files, rather than code, to define, train, evaluate, and visualize models. While the usefulness of this exact tool has yet to be seen, it did get me thinking about [abstraction](https://en.wikipedia.org/wiki/Abstraction_(computer_science)) (in the computer science sense).

To put the idea into tip form: abstract as much as possible/don't implement anything you don't have to/start with the highest level tool you can/write as little code as possible/keep your error surface as small as possible.

With newer tools such as Ludwig and Keras (and many more I can mention), the implementation details are further abstracted away, allowing one to focus on the task at hand. I think we should recommend using the highest level tools that can accomplish the task, especially for those new to deep learning. This would have certainly saved me a lot of time and I'm interested in hearing what others think about this.

Start by reviewing the seven-comment discussion and the linked list of proposed tips; contributors.md is the only repository file named. Identify whether there is agreement on the abstraction guidance and where the manuscript expects new tips. Done means a maintainer-approved, appropriately scoped tip has been added to the project.

Written by the indexing model from the issue text.