Optimization of Neural Networks Using Advanced Hyperparameter Tuning

Abstract

Neural networks have become an essential tool for solving complex tasks in domains such as image recognition, natural language processing, and autonomous systems. However, the performance of neural networks heavily depends on the choice of hyperparameters such as learning rate, batch size, activation functions, and the number of hidden layers. Hyperparameter optimization, therefore, plays a critical role in enhancing model accuracy, convergence speed, and generalization capabilities. Traditional tuning methods like manual selection and grid search are often computationally expensive and suboptimal for large-scale networks. In this study, we explore advanced hyperparameter tuning strategies including Random Search, Bayesian Optimization, Hyperband, and Genetic Algorithms, evaluating their efficiency and effectiveness on various benchmark datasets. The research presents a comprehensive methodology integrating automated hyperparameter selection with neural network training, highlighting the trade-offs between computational cost and model performance. Experimental results demonstrate that optimized hyperparameters significantly improve the accuracy and stability of neural networks, reducing overfitting and training time. This paper also proposes a systematic framework for hyperparameter optimization that can guide practitioners and researchers in selecting optimal configurations tailored to their specific problem domains. By comparing the performance across different tuning strategies, we offer practical insights into the scalability and adaptability of neural network optimization techniques. The findings underscore the importance of leveraging intelligent hyperparameter optimization methods to advance deep learning applications and achieve superior performance in real-world scenarios.