Research and optimisation of deep learning algorithms for big data

Abstract

This research is dedicated to the in-depth study and optimisation of deep learning algorithms for big data environments. With the continuous development of big data technology, deep learning algorithms have achieved remarkable results in processing large-scale, high-dimensional and diverse data. However, in the context of big data, there are still problems such as low training efficiency, insufficient model generalisation performance, and excessive resource consumption, which urgently require in-depth research to enhance the applicability of deep learning algorithms in big data applications. First, we review the basic principles and architectures of deep learning algorithms and explore the challenges they face in the big data environment. In the review of related work, we summarise the current status of big data and deep learning research, review existing optimisation algorithms, and point out the problems and research gaps. By analysing the characteristics of big data, including data size, diversity, real-time requirements, and storage and computation constraints, we delve into the impact of these characteristics on deep learning, providing theoretical support for subsequent research. In the research methodology and algorithm design section, we propose a series of deep learning algorithm optimisation strategies for big data environments, including distributed and parallel computing, big data sampling and preprocessing, parameter optimisation and tuning, and model architecture tuning and innovation. Through the comprehensive application of these strategies, we aim to improve the efficiency and performance of deep learning algorithms in big data environments. Through experimental design and evaluation, we select representative big data sets for validation and use multiple evaluation metrics to comprehensively assess the optimised algorithms. Through comparative experiments and performance analyses with traditional methods, we demonstrate the superior performance of the proposed algorithm. Finally, by interpreting and discussing the experimental results, we summarise the main research findings and propose directions for future improvement and outlooks for in-depth research. The results of this study are not only of guiding significance for the application of deep learning algorithms in big data environments, but also provide useful theoretical support and practical experience for research in related fields.