期刊文献+

高职《深度学习》课程教学的实施 被引量:1

Teaching Implementation on the〈Deep Learning〉Course in Higher Vocational Colleges
下载PDF
导出
摘要 随着深度学习技术的飞速发展,人工智能技术发展实现从理论到实际应用,其应用领域也不断扩大,作为核心技术的深度学习逐渐成为培养人才和高等院校教育的重要关注点。深度学习具有内容多、难度大、更新快的特点。本文分析深度学习的主要应用范畴,结合高职人工智能技术与应用专业培养目标,对高职院校人工智能相关专业《深度学习》课程教学实施进行了相关探索。 With the rapid development of deep learning technology,the development of artificial intelligence technology has realized from theory to practical application,and its application field is also expanding.As the core technology,deep learning has become gradually an important focus of talent training and higher education.Deep learning possesses the characteristics of many contents,great difficulty and fast update.This paper analyzes the main application areas of deep learning,combined with the training objectives of artificial intelligence technology and application specialty in higher vocational colleges,and explores the teaching implementation of〈Deep Learning〉in artificial intelligence related majors in higher vocational colleges.
作者 杨灿 YANG Can(Hunan Vocational College of Science and Technology,Changsha 410004)
出处 《办公自动化》 2021年第16期37-38,共2页 Office Informatization
基金 2020年度湖南省教育厅科学研究项目,编号:20C0863,项目名称:基于深度神经网络的多源空间数据融合方法研究 2019年湖南科技职业学院校级科研课题,编号:KJ19218,课题名称:基于深度学习的多源影像数据融合方法研究
关键词 深度学习 教学实施 deep learning teaching implementation
  • 相关文献

参考文献7

二级参考文献99

  • 1BENGIO Y, DELALLEAU O. On the expressive power of deep archi- tectures[ C ]//Proc of the 14th International Conference on Discovery Science. Berlin : Springer-Verlag, 2011 : 18 - 36.
  • 2BENGIO Y. Leaming deep architectures for AI[ J]. Foundations and Trends in Machine Learning ,2009,2 ( 1 ) : 1-127.
  • 3HINTON G,OSINDERO S,TEH Y. A fast learning algorithm for deep belief nets [ J ]. Neural Computation ,2006,18 (7) : 1527-1554.
  • 4BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks [ C ]//Proc of the 12th Annual Conference on Neural Information Processing System. 2006:153-160.
  • 5LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning ap- plied to document recognition[ J]. Proceedings of the iEEE, 1998, 86( 11 ) :2278-2324.
  • 6VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[ C ]//Proc of the 25th International Conference on Machine Learning. New York: ACM Press ,2008 : 1096-1103.
  • 7VINCENT P, LAROCHELLE H, LAJOIE I, et aL Stacked denoising autoencoders:learning useftd representations in a deep network with a local denoising criterion [ J ]. Journal of Machine Learning Re- search ,2010,11 ( 12 ) :3371-3408.
  • 8YU Dong, DENG Li. Deep convex net: a scalable architecture for speech pattern classification [ C]//Proc of the 12th Annual Confe-rence of International Speech Comunication Association. 2011 : 2285- 2288.
  • 9POON H, DOMINGOS P. Sum-product networks:a new deep architec- ture[ C ]//Proc of IEEE Intemational Conference on Computer Vi- sion. 2011:689-690.
  • 10BENGIO Y,LECUN Y. Scaling learning algorithms towards AI[ M]// BOTTOU L,CHAPELLE O, DeCOSTE D,et al. Large-Scale Kernel Machines. Cambridge: MIT Press ,2007:321-358.

共引文献735

同被引文献8

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部