Cao, D., & Lowell, C., & Morris, A. (2018, June), Undergraduate Research: Introducing Deep Learning-based Image Classification to Undergraduate Students  Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--31171

\bibitem{asee_peer_31171}
  Cao, D., \& Lowell, C., \& Morris, A. (2018, June), \emph{Undergraduate Research: Introducing Deep Learning-based Image Classification to Undergraduate Students}
  Paper presented at 2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah.
  10.18260/1-2--31171

Deng Cao, Cadance Lowell, and Augustus Morris.  "Undergraduate Research: Introducing Deep Learning-based Image Classification to Undergraduate Students".  2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah, 2018, June.  ASEE Conferences, 2018.  https://strategy.asee.org/31171 Internet.  29 Jul, 2024

\bibitem{asee_peer_31171}
  Deng Cao, Cadance Lowell, and Augustus Morris.
  "Undergraduate Research: Introducing Deep Learning-based Image Classification to Undergraduate Students".
  \emph{2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah, 2018, June}.
  ASEE Conferences, 2018.
  https://strategy.asee.org/31171 Internet.  29 Jul, 2024

@INPROCEEDINGS{asee_peer_31171
author = "Deng Cao, Cadance Lowell, and Augustus Morris"
title = "Undergraduate Research: Introducing Deep Learning-based Image Classification to Undergraduate Students"
booktitle = "2018 ASEE Annual Conference \& Exposition "
year = "2018"
month = "June"
address = "Salt Lake City, Utah"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/31171}
number = {10.18260/1-2--31171}
}

TY  - CPAPER
AB  - In the past few years, deep learning based methods has quickly become the state of the art in image classification and object detection. As one of the best deep learning structures, Convolutional Neural Network (CNN) is highly automated and requires little prior knowledge. Also, a customized CNN can be quickly built without a large database, if a pre-trained network is provided. These advantages make CNN suitable for undergraduate research. Funded by an 1890 Land Grant Research Project III, CNN is introduced to the undergraduate students in our institution and the students are trained to develop customized CNN in order to solve given image classification problems. The achieved goals and discovered issues are reported and discussed in this work. Overall, the results demonstrated a positive example of integrating modern technology and research into undergraduate classrooms.
AU  - Deng Cao
AU  - Cadance Lowell
AU  - Augustus Morris
CY  - Salt Lake City, Utah
DA  - 2018/06/23
PB  - ASEE Conferences
TI  - Undergraduate Research: Introducing Deep Learning-based Image Classification to Undergraduate Students
UR  - https://strategy.asee.org/31171
DO  - 10.18260/1-2--31171
ER  -