Neural Network Optimization Techniques: Studying optimization techniques for training neural networks, including gradient-based methods and metaheuristic algorithms

Abstract

Neural networks have demonstrated remarkable success in various machine learning tasks, but training them efficiently remains a significant challenge. Optimization techniques play a crucial role in improving the training process by minimizing the loss function. This paper provides a comprehensive overview of neural network optimization techniques, including both traditional gradient-based methods and more recent metaheuristic algorithms. We discuss the theoretical foundations, advantages, and limitations of these techniques, along with practical considerations for their implementation. Furthermore, we present a comparative analysis of these techniques based on their convergence properties, computational efficiency, and performance on benchmark datasets. This paper aims to provide researchers and practitioners with a deeper understanding of neural network optimization, enabling them to select the most suitable techniques for their applications.