Congratulations, you've completed the second chapter of the **PyAutoLens** tutorials!

In this chapter, you learnt:

1) What a `NonLinearSearch` is.
2) How to use a `NonLinearSearch` to sample the parametric space of a lens model.
3) How the priors we give parameters specify our search of this parameter space.
4) How one must carefully balance complexity and realism when fitting a model to a data-set.
5) The importance of factoring in run-speed when modeling a lens, and tricks to speed up the analysis.
6) About masking data and using positions in the analysis.

At this point, you are actually ready to begin modeling lenses with **PyAutoLens**. The 'examples' folder in the
autolens_workspace contains a number of scripts that can be easily adopted to model lens using a variety of different
approaches, lens model and non-linear searches. If you have your own lens data, I'd recommend you adapt these
scripts to your data. If you don't have your own data, checkout the 'autolens_workspace/simulators' folder to simulate
your own dataset!

Alternatively, you m ay wish to continue on to chapter 3 on pipelines, which I've hyped-up multiple times throughout
this chapter. Pipelines are where **PyAutoLens** really comes into its own: if you arn't using pipelines you arn't
exploiting **PyAutoLens**'s most powerful features!