Neural architecture search automates the design of neural network architectures by using algorithms — often itself powered by machine learning — to explore a vast space of possible architectures and find ones that perform best on a given task. Instead of a human researcher deciding the number of layers, filter sizes, and connection patterns, NAS discovers them automatically. It has produced architectures that outperform human-designed ones, though the search process itself can be computationally expensive.