  Intelligent reflecting surfaces (IRSs) have recently received significant
attention for wireless communications because it reduces the hardware
complexity, physical size, weight, and cost of conventional large arrays.
However, deployment of IRS entails dealing with multiple channel links between
the base station (BS) and the users. Further, the BS and IRS beamformers
require a joint design, wherein the IRS elements must be rapidly reconfigured.
Data-driven techniques, such as deep learning (DL), are critical in addressing
these challenges. The lower computation time and model-free nature of DL makes
it robust against the data imperfections and environmental changes. At the
physical layer, DL has been shown to be effective for IRS signal detection,
channel estimation and active/passive beamforming using architectures such as
supervised, unsupervised and reinforcement learning. This article provides a
synopsis of these techniques for designing DL-based IRS-assisted wireless
systems.
