Gradient Descent Algorithm Survey
NeutralArtificial Intelligence
- A recent survey on optimization algorithms in deep learning has been published, focusing on five key methods: SGD, Mini-batch SGD, Momentum, Adam, and Lion. The study systematically evaluates their advantages, limitations, and practical recommendations, aiming to enhance the understanding and application of these algorithms in various training scenarios.
- This research is significant as it provides a standardized reference for selecting and tuning optimization algorithms, which is crucial for improving model performance in both academic and engineering contexts, particularly as deep learning continues to evolve.
- The exploration of these algorithms aligns with ongoing discussions in the field regarding the effectiveness of adaptive optimizers and the challenges posed by decentralized and federated learning environments. Insights from this survey contribute to a broader understanding of optimization strategies, particularly in addressing issues like convergence rates and training stability.
— via World Pulse Now AI Editorial System

