Guidelines for adding new examples

So this issue gives out a template or a guide that can be followed or kept in mind when writing new examples:

• The examples should be well documented in terms that can easily be understood by people entering the field of Machine learning or have been in it for a long time.

• Make examples simple enough so the user can basically grasp what he needs to build his own full-fledged model and don't over-complicate the examples but still try to include all library functionalities related to that example.

• A little bit more can be written about parameters or functionalities which are implemented differently in mlpack than what the common notion is or which might confuse the user.

• When adding the comments and writing tutorials try to mention why you followed a particular strategy in the example and maybe mention what are the implications of the strategy that you took and what are the other ways a user can proceed with the example.

• Some examples that show things that are particularly interesting or practical can take a divergence from these guidelines and can become a bit complicated, maybe an example of such could be GANs but not limited to it.

• And please take a look at the example that exists before and try to avoid redundant examples.

And lastly, this is just a guide, not strict rules so have fun writing examples and show your creativity. :)

Review the existing examples before proposing a guide, as the issue asks contributors to avoid redundant examples and explain practical machine-learning strategies. The guide should cover clarity, simplicity, relevant library functionality, parameter explanations, and alternative strategies; its final location and format are not specified in the issue.