Optimizing Neural Networks: Techniques for Improving Accuracy and Reducing Over fitting
Optimizing Neural Networks: Techniques for Improving Accuracy and Reducing Over fitting
Authors:
Dr. Nittala Ramachandra¹, Goundla Adithya Goud²
¹Professor, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India kurumallaasuresh@gmail.com
2Student, Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, India adithyagoud257@gmail.com
Abstract: Neural networks have become a cornerstone of modern artificial intelligence applications, achieving remarkable success in areas such as image recognition, natural language processing, and predictive analytics. However, achieving high accuracy while avoiding overfitting remains a major challenge in deep learning systems. This paper explores various optimization techniques for improving neural network performance, including regularization methods, hyperparameter tuning, data augmentation, and advanced architectures. The study reviews approaches such as dropout, batch normalization, early stopping, and ensemble learning to enhance generalization. Additionally, the role of optimization algorithms like stochastic gradient descent (SGD), Adam, and RMSProp is discussed. Evaluation metrics such as accuracy, precision, recall, and loss functions are used to measure performance. Challenges such as model complexity, computational cost, and data imbalance are also addressed. The paper concludes with future directions focusing on automated machine learning (AutoML) and explainable AI. Overall, optimizing neural networks plays a crucial role in developing robust and reliable AI systems.
Keywords: Neural Networks, Overfitting, Regularization, Deep Learning, Optimization Techniques, Dropout, Batch Normalization, Hyperparameter Tuning