Now that we have understood the architecture of GANs, let's take a look at a brief overview of a few important concepts. We will first look at KL divergence. It is very important to understand JS divergence, which is an important measure to assess the quality of the models. We will then look at the Nash equilibrium, which is a state that we try to achieve during training. Finally, we will look closer at objective functions, which are very important to understand in order to implement GANs well.