  Neural Architecture Search (NAS) is a research field concerned with utilizing
optimization algorithms to design optimal neural network architectures. There
are many approaches concerning the architectural search spaces, optimization
algorithms, as well as candidate architecture evaluation methods. As the field
is growing at a continuously increasing pace, it is difficult for a beginner to
discern between major, as well as emerging directions the field has followed.
In this work, we provide an introduction to the basic concepts of NAS for
convolutional networks, along with the major advances in search spaces,
algorithms and evaluation techniques.
