Generative Adversarial Networks and WaveNet

In this chapter, we will discuss generative adversarial networks (GANs) and WaveNets. GANs have been defined as the most interesting idea in the last 10 years in ML (https://www.quora.com/What-are-some-recent-and-potentially-upcoming-breakthroughs-in-deep-learning) by Yann LeCun, one of the fathers of deep learning. GANs are able to learn how to reproduce synthetic data that looks real. For instance, computers can learn how to paint and create realistic images. The idea was originally proposed by Ian Goodfellow (for more information refer to: NIPS 2016 Tutorial: Generative Adversarial Networks, by I. Goodfellow, 2016); he was worked with the University of Montreal, Google Brain, and recently OpenAI (https://openai.com/). WaveNet is a deep generative network proposed by Google DeepMind to teach computers how to reproduce human voices and musical instruments...