A Statistician Teaches Deep Learning
A Statistician Teaches Deep Learning
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DOI：
10.1007/s42519-021-00193-0
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发表时间：
2021-03-23
影响因子：
0.6
通讯作者：
Wang, Shouyi
中科院分区：
文献类型：
--
作者：
Babu, G. Jogesh;Banks, David;Wang, Shouyi
深度学习（Deep learning， DL）在现代数据科学中越来越受到关注和欢迎。计算机科学家在开发深度学习技术方面处于领先地位，因此这些想法和观点对统计学家来说似乎是陌生的。尽管如此，统计学家的参与是很重要的——我们的许多学生在他们的职业生涯中需要这些专业知识。在本文中，作为统计与应用数学科学研究所举办的深度学习项目的一部分，我们解决了这种文化差距，并提供了如何向统计研究生教授深度学习的技巧。在一些背景之后，我们列出了深度学习和统计观点的不同之处，提供了一个从深度学习研究生课程的两个迭代中演变而来的推荐教学大纲，提供了建议的家庭作业示例，给出了一个教学资源的注释列表，并在两个研究领域的背景下讨论了深度学习。
Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the ideas and perspectives can seem alien to statisticians. Nonetheless, it is important that statisticians become involved-many of our students need this expertise for their careers. In this paper, developed as part of a program on DL held at the Statistical and Applied Mathematical Sciences Institute, we address this culture gap and provide tips on how to teach deep learning to statistics graduate students. After some background, we list ways in which DL and statistical perspectives differ, provide a recommended syllabus that evolved from teaching two iterations of a DL graduate course, offer examples of suggested homework assignments, give an annotated list of teaching resources, and discuss DL in the context of two research areas.