Neural Architecture Search

Neural Architecture Search (NAS) is a subfield of machine learning and artificial intelligence that focuses on automating the design of neural networks. NAS algorithms use search techniques such as reinforcement learning, evolutionary algorithms, and gradient-based methods to explore the space of possible neural network architectures and find the optimal architecture for a given task. NAS has the potential to significantly reduce the time and resources required for designing neural networks, and has been successfully applied to a variety of tasks such as image classification, object detection, and natural language processing.
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