At the end of the introduction tutorials, we could fit strong lensing `Imaging` with a `Tracer`. However, we simulated the
image ourselves, which meant we knew the combination of `LightProfile`'. and `MassProfile`'. provided a good fit.

In this set of tutorials, we'll deal the situation that we face in the real world and assume we have no knowledge of
which solutions actually provide a good fit. This process of finding the good solutions is called 'lens modeling',
and its what we wrote **PyAutoLens** to do.

At the end of tutorials you will understand:

1) The concept of a `NonLinearSearch` and non-linear parameter space.
2) How to perform a `NonLinearSearch` with **PyAutoLens** to fit a lens model.
3) The trade-off between realism and complexity when choosing a lens model.
4) Why an incorrect lens model may be inferred and how to prevent this from happening.
5) The challenges that are involved in inferred a robust lens model in a computationally reasonable run-time.
