Analysis of machine learning: enhancing performance and interpretability through deep neural network
Saloni Bansal, Birendra Kr Saraswat, Vijay Kr Sharma · 2024
This study explores the use of deep neural networks (DNNs) and explainable AI strategies to improve performance and interpretability in current developments in machine learning. The exponential increase in data and computer resources has made it possible to create DNN designs that are ever more sophisticated, greatly enhancing learning capabilities. Additionally, the problem of model interpretability has been addressed with the addition of explainable AI methodologies, allowing users to learn more about how DNNs make decisions. The potential for further developments in the discipline is highlighted as this study examines several approaches and tactics used to improve the performance and interpretability of machine learning models.