Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Statistician Teaches Deep Learning   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.

0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg