Detailed Study of Supervised Learning Algorithms and Their Applications in Real-World Scenarios
S Praveena · 2024
Neural Architecture Search (NAS) has revolutionized the design of deep learning models by automating the exploration of neural network architectures, thereby enhancing performance across various domains. This chapter delves into the latest advancements in NAS, focusing on its application in image classification, natural language processing, autonomous systems, and hardware optimization. Key methodologies, including reinforcement learning-based and efficient NAS approaches, are explored in depth to illustrate their impact on model accuracy and computational efficiency. Through comprehensive case studies, the chapter highlights the transformative potential of NAS in generating state-of-the-art architectures, optimizing resource utilization, and addressing complex tasks with unprecedented precision. The discussion emphasizes the balance between search efficiency and model performance, providing insights into the future trajectory of NAS research. This chapter was essential for understanding the cutting-edge techniques and practical applications of NAS, offering valuable knowledge for researchers and practitioners in the field of machine learning and artificial intelligence.