In this chapter, we discussed how to create new images with generative models, which is one of the most exciting deep learning areas at the moment. We learned about the theoretical foundations of VAEs and then we implemented a simple VAE to generate new MNIST digits. Then, we described the GAN framework and we discussed and implemented multiple types of GAN, including DCGAN, CGAN, WGAN, and CycleGAN. Finally, we mentioned the neural style transfer algorithm. This chapter concludes a series of four chapters dedicated to computer vision and I really hope you've enjoyed them.
In the next few chapters, we'll talk about Natural Language Processing and recurrent networks.
