Despite its transformative potential, the adoption of Deep Learning remains very limited in resource-limited applications, mainly because of the need for significant expertise, personal experience, and intuitive understanding for an effective design of architectures. With the theoretical understanding of neural architectures still in its nascent stages, designing deep models often entails an empirical approach of testing a multitude of architectures – a process that is simply impractical in many scenarios. The research community has made concerted efforts in recent years to streamline and automate this critical yet often tedious aspect of AI research. The field of Neural Architecture Search (NAS) specifically focuses on this issue.