In this chapter, our scope has expanded even more, adding the important dimension of time to the set of elements to be included in our generalization. Also, we learned how to solve a practical problem with RNNs, based on real data.
But if you think you have covered all the possible options, there are many more model types to see!
In the next chapter, we will talk about cutting edge architectures that can be trained to produce very clever elements, for example, transfer the style of famous painters to a picture, and even play video games! Keep reading for reinforcement learning and generative adversarial networks.
