Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization

Search (NAS) represent a transformative phase in artificial intelligence, particularly in deep learning. The quest for The trajectory of NAS research from its early days to its current automating the design of neural network architectures has status underscores a broad and ambitious effort to automate seen significant milestones, with research efforts focusing on and optimize the design of neural networks across various overcoming the limitations of manual architecture design and domains. From enhancing LSTM networks to pioneering in the leveraging computational strategies to discover optimal network convolutional neural network (CNN) architectures and extending structures. Early research in the domain of NAS was marked to medical and language processing applications, NAS embodies by efforts to understand and improve recurrent neural networks, the transition from manual, expert-driven design to automated, such as the Long Short-Term Memory (LSTM) networks computationally-driven architecture search processes.