Optimizing Data Quality in Deep Learning through Advanced Analytics

This paper explores the vital role of data quality in deep learning, introducing an innovative method for evaluating and optimizing dataset quality through deep learning analytics. It underscores the necessity of high-quality data for deep learning success and critiques current data quality evaluation techniques. A novel framework is proposed, utilizing deep learning for both data quality assessment and improvement, tailored to meet the unique challenges of deep learning datasets, such as variability and complexity. The paper outlines this framework with specific evaluation metrics and optimization strategies, validated by real-world case studies. The findings demonstrate the effectiveness of deep learning analytics in enhancing data quality, offering significant contributions to the field by enabling more precise and efficient deep learning models through practical data quality improvement solutions.