In this chapter, we looked at GANs and how they can be used to generate new images. We learned a few rules for building GANs well, and we even learned to simulate MNIST and CIFAR-10 images. There is no doubt that you've probably seen some amazing images, created by GANs, in the media. After reading this chapter and working through these examples, you have the tools to do the same. I hope that you can take these ideas and adapt them. The only limitations left are your own imagination, your data, and your GPU budget.

In this book we covered a great many applications of deep learning, from simple regression to Generative Adversarial Networks. My greatest hope for this book is that it might help you make practical use of deep learning techniques, many of which have existed in the domain of academia and research, outside the reach of the practicing data scientist or machine learning engineer. Along the way I hope I might have given you some advice on how to build better deep neural networks, and when to use a deep network as opposed to a more traditional model. If you've followed along with me through out these 13 chapters, thank you for you reading.