Deep learning is a modern approach solving many problems that feature a lot of data. Nevertheless, it is extremely difficult to understand the behaviour of trained networks. In this talk, I will cover some novel methods which are in many cases the state-of-the-art and may help to understand networks better. Furthermore, I will present some research directions which might improve the overall interpretability of the deep learning models.