Deep Learning Error Minimization System for Real-Time Big Data Analysis in Mobile Applications

This paper presents a novel deep learning error minimization system designed to enhance the efficiency, adaptability, and accuracy of real-time big data analysis in mobile applications. Traditional deep learning systems face limitations such as the need for large labeled datasets, high computational complexity, and challenges in generalizing to new tasks and dynamic data. The proposed system addresses these limitations through a hybrid neural network architecture that integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs), a dynamic error feedback mechanism using reinforcement learning, and a lightweight transfer learning module. The system also includes modules for data preprocessing, augmentation, and model evaluation. Case studies demonstrate the system's effectiveness in real-time data analysis for applications such as financial market analysis, showcasing its potential to significantly improve performance and user experience in mobile applications.