Research on Curriculum Design Method of Teaching Resource Library based on Deep Learning Technology
DOI:
https://doi.org/10.54097/hset.v35i.7048Keywords:
Curriculum Design; Deep Learning; DPL Algorithm; Transfer Learning.Abstract
In order to improve the ideological understanding of computer majors and establish a correct world outlook, outlook on life and values, the ideological and political goals of computer majors are designed; (1) A new deep learning face recognition algorithm is proposed. Aiming at the limitations of many current methods of artificially designing image description features, this algorithm proposes an unsupervised big data-driven deep image feature learning and representation mechanism. (2) A new idea of single-target tracking algorithm DPL is proposed. Single target tracking is a hot research topic in the field of computer anxiety. Our proposed DPL algorithm is still very competitive with the best algorithms in the field of object tracking. Several key technology research and model design schemes for computer major courses are proposed, which enriches the relevant research content of deep learning in the field of computer vision, and has certain theoretical and practical significance.
Downloads
References
Onizuka M, Kato H, Hidaka S, et al. Optimization for iterative queries on MapReduce. Pro-ceedings of the VLDB Endowment,2013,7(4).
Kwon Y, Balazinska M, Howe B, et al. Skewtune: mitigating skew in mapreduce applications. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. ACM, 2012. 25-36.
Abadi D J, Carney D, Cetintemel U, et al. Aurora: A New Model and Architecture for Data Stream Management. The VLDB Journal, 2003,12(2):120-139.
Ahmad Y, Berg B, Cetintemel U, et al. Distributed Operation in the Borealis Stream Process-ing Engine. Proceedings of the 2005 ACM SIGMOD International Conference on Manage-ment of Data. ACM, 2005. 882-884.
Chen J, DeWitt D J, Tian F, et al. NiagaraCQ: A Scalable Continuous Query System for Internet Databases. Proceedings of the 2000 ACM SIGMOD International Conference onManagement of Data.ACM, 2000.379-390.
Tuning Spark. http://spark.apache.org/docs/1.2.1/tuning.html.
Kyrola A, Blelloch G E, Guestrin C. GraphChi: Large-Scale Graph Computation on Just a PC. oSDI, volume 12, 2012.31-46.
Chen G L, Sun G Z, Zhang Y Q, et al. Study on parallel computing. Journal of Computer Science and Technology, 2006,21(S):665-673.
Fortune S, Wyllie J. Parallelism in random access machines. Proceedings of the tenth annual ACM symposium on Theory of computing.ACM,1978.114-118.
Gibbons P B. A more practical PRAM model. Proceedings of the first annual ACM sympo-sium on Parallel algorithms and architectures.ACM,1989.158-168.
Culler D, Karp R, Patterson D, et al. LogP: Towards a realistic model of parallel computation, volume 28.ACM, 1993.
Zhang Y, Chen G, Sun G, et al. Models of parallel computation: a survey and classification. Frontiers of Computer Science in China, 2007,1(2):156-165.
Qiao X. Algorithm Optimization on HPM Model. PDPTA, 2003.365-371.
Huai Y, Lee R, Zhang S, et al. DOT: a matrix model for analyzing, optimizing and deploying software for big data analytics in distributed systems. Proceedings of the 2nd ACM Sympo-sium on Cloud Computing. ACM, 2011.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







