Author(s)

Hantao Chen ¹

Affiliation(s)

¹ School of Information and Intelligent Engineering, Guangzhou Xinhua University, Dongguan, 523133, China

Corresponding Author

Hantao Chen

ABSTRACT

With the explosive growth of data volumes and the increasing demands for computation, distributed computing plays an increasingly important role in the fields of machine learning and deep learning. This paper comprehensively analyzes the practical applications of machine learning and deep learning based on distributed computing, discussing their basic concepts, current application status, main challenges, and future development strategies. Through detailed comparative analysis of theory and practice, this paper aims to provide valuable references and strategic recommendations for the related fields.

KEYWORDS

Distributed Computing; Machine Learning; Deep Learning; Practical Applications; Performance Optimization

CITE THIS PAPER

Hantao Chen, Research on Practical Applications of Machine Learning and Deep Learning Based on Distributed Computing. Advances in Computer, Signals and Systems (2024) Vol. 8: 39-45. DOI: http://dx.doi.org/10.23977/acss.2024.080406.

REFERENCES

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[2] Hu Changli, Shao Jianfei. Analysis of Machine Learning Systems Based on Distributed Computing Frameworks [J]. Television Technology. 2021, 45(11):115-118+123.
[3] Zhang Hangang, Deng Xinyuan, Song Ye, Xue Xuwei, Guo Bingli, Huang Shanguo. Communication Optimization Techniques for Distributed Machine Learning Networks [J]. Postal Design Technology. 2024(02):27-30.
[4] Zhu Jiayi. Parallel Training Methods for Machine Learning and Deep Learning [J]. Modern Computer. 2022, 28(14):42-48.
[5] Bai Minghui, Yuan Shaojun. Research on Distributed Generation Grid Connection Protection Based on Machine Learning [J]. Microcomputer Applications. 2023, 39(12):73-76.
[6] Xing Feng, Liu Xingxu. Practical Application of Machine Learning in Data Analysis. Telecommunications Engineering Technology and Standardization [J]. 2021, 34(12): 82-84+88. 

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