  Nanophotonics has been an active research field over the past two decades,
triggered by the rising interests in exploring new physics and technologies
with light at the nanoscale. As the demands of performance and integration
level keep increasing, the design and optimization of nanophotonic devices
become computationally expensive and time-inefficient. Advanced computational
methods and artificial intelligence, especially its subfield of machine
learning, have led to revolutionary development in many applications, such as
web searches, computer vision, and speech/image recognition. The complex models
and algorithms help to exploit the enormous parameter space in a highly
efficient way. In this review, we summarize the recent advances on the emerging
field where nanophotonics and machine learning blend. We provide an overview of
different computational methods, with the focus on deep learning, for the
nanophotonic inverse design. The implementation of deep neural networks with
photonic platforms is also discussed. This review aims at sketching an
illustration of the nanophotonic design with machine learning and giving a
perspective on the future tasks.
