ENNORM

ENNORM, an acronym for "Efficient Neural Network Optimization and Regularization Method," is a technique designed to enhance the performance and generalization of neural networks. It combines principles from both optimization and regularization to achieve this goal. The method involves modifying the training process by incorporating additional terms into the loss function, which encourage the network to learn more robust and generalizable features. These terms often penalize large weights or complex models, thereby preventing overfitting and improving the network's ability to generalize to unseen data. ENNORM also employs advanced optimization algorithms that adaptively adjust the learning rate and other hyperparameters during training, leading to faster convergence and better performance. By integrating these elements, ENNORM aims to strike a balance between model complexity and generalization, making it a valuable tool in the field of machine learning and artificial intelligence.