Neural architecture search (NAS) is an automated process used to design and optimize neural network architectures. Rather than manually selecting hyperparameters and model architectures, NAS algorithms explore different configurations and architectures to identify the most suitable one for a given task.
This process typically involves search methods like reinforcement learning, evolutionary algorithms, or gradient-based optimization to generate and evaluate possible architectures. The goal is to find a high-performing model architecture with the least human intervention.
NAS is widely used in tasks where optimal architecture plays a significant role in model performance, such as image classification and natural language processing.