Summary

In this chapter we have seen many applications of CNNs across very different domains, from traditional image processing and computer vision, to close-enough video processing, to not-so-close audio processing and text processing. In a relatively few number of years, CNNs took machine learning by storm.

Nowadays it is not uncommon to see multimodal processing, where text, images, audio, and videos are considered together to achieve better performance, frequently by means of CNNs together with a bunch of other techniques such as RNNs and reinforcement learning. Of course, there is much more to consider, and CNNs have recently been applied to many other domains such as Genetic inference [13], which are, at least at first glance, far away from the original scope of their design.

In this chapter, we have discussed all the major variants of ConvNets. In the next chapter, we will introduce Generative Nets: one of the most innovative deep learning architectures yet.