  Deep learning in the context of nano-photonics is mostly discussed in terms
of its potential for inverse design of photonic devices or nanostructures. Many
of the recent works on machine-learning inverse design are highly specific, and
the drawbacks of the respective approaches are often not immediately clear. In
this review we want therefore to provide a critical review on the capabilities
of deep learning for inverse design and the progress which has been made so
far. We classify the different deep learning-based inverse design approaches at
a higher level as well as by the context of their respective applications and
critically discuss their strengths and weaknesses. While a significant part of
the community's attention lies on nano-photonic inverse design, deep learning
has evolved as a tool for a large variety of applications. The second part of
the review will focus therefore on machine learning research in nano-photonics
"beyond inverse design". This spans from physics informed neural networks for
tremendous acceleration of photonics simulations, over sparse data
reconstruction, imaging and "knowledge discovery" to experimental applications.
