Neural Architecture Search (NAS) is an innovative approach in machine learning that automates the design of neural network architectures. It has gained significant attention due to its ability to optimize model performance without requiring extensive manual tuning. The process involves exploring various architectures to identify the most effective one for a specific task, such as image classification or natural language processing.

The NAS process can be broken down into three main dimensions: search space, search strategy, and performance estimation. The search space defines the possible architectures that can be explored, while the search strategy determines how these architectures are evaluated and selected. Performance estimation is crucial for assessing the effectiveness of the architectures during the search process, ensuring that the best-performing models are identified efficiently ⁵.

Commercial services like Google’s AutoML and open-source libraries such as Auto-Keras have made NAS more accessible, allowing developers to leverage these advanced techniques in their projects ².