@inproceedings{e993aece4d9d4cb081aa65f13e637b0b,
title = "Notes on Using Google Colaboratory in AI Education",
abstract = "We discuss our experiences using Google Colaboratory (Colab), a hosted version of Jupyter Notebooks, in undergraduate artificial intelligence (AI) courses at two universities. Colab was designed for AI and data science researchers to share reproducible experiments and explanations of techniques, but we have also found it well suited to classroom use. The primary benefit is that it provides students computational resources sufficient to run modern AI techniques interactively, and avoids students needing to separately configure software packages and dependencies, since they can run notebooks shared by the instructor. We briefly outline two of our notebooks, for teaching deep learning with Tensorflow, and reinforcement learning with OpenAI Gym.",
keywords = "AI education, Google colab, Jupyter notebook, notebook interface",
author = "Nelson, {Mark J.} and Hoover, {Amy K.}",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 25th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2020 ; Conference date: 15-06-2020 Through 19-06-2020",
year = "2020",
month = jun,
day = "15",
doi = "10.1145/3341525.3393997",
language = "English (US)",
series = "Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE",
publisher = "Association for Computing Machinery",
pages = "533--534",
booktitle = "ITiCSE 2020 - Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education",
}