https://carpentries-incubator.github.io/machine-learning-librarians-archivists/

Introduction to AI for GLAM
https://carpentries-incubator.github.io/machine-learning-librarians-archivists/

beta carpentries-incubator english glam lesson machine-learning

Last synced: 3 months ago
JSON representation

Introduction to AI for GLAM

• Host: GitHub
• URL: https://carpentries-incubator.github.io/machine-learning-librarians-archivists/
• Owner: carpentries-incubator
• License: other
• Created: 2021-02-11T09:45:30.000Z (over 3 years ago)
• Default Branch: gh-pages
• Last Pushed: 2024-05-13T17:37:31.000Z (6 months ago)
• Last Synced: 2024-05-23T01:32:41.731Z (6 months ago)
• Topics: beta, carpentries-incubator, english, glam, lesson, machine-learning
• Language: Python
• Homepage: https://carpentries-incubator.github.io/machine-learning-librarians-archivists/
• Size: 26 MB
• Stars: 17
• Watchers: 11
• Forks: 15
• Open Issues: 31
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION
  • Authors: AUTHORS

Awesome Lists containing this project

• awesome-ai4lam - Introduction to AI for GLAM

README

        
# Intro to AI for GLAM

[![Create a Slack Account with us](https://img.shields.io/badge/Create_Slack_Account-The_Carpentries-071159.svg)](https://swc-slack-invite.herokuapp.com/)

Our aim with this lesson is to empower GLAM ([Galleries, Libraries, Archives, and Museums](https://en.wikipedia.org/wiki/GLAM_(industry))) staff with the foundation to support, participate in and begin to undertake in their own right, machine learning based research and projects with heritage collections. 

After following this lesson, learners will be able to:

* Explain and differentiate key terms, phrases, and concepts associated with AI and Machine Learning in GLAM

* Describe ways in which AI is being innovatively used in the cultural heritage context today

* Identify what kinds of tasks machine learning models excel at in GLAM applications

* Identify weaknesses in machine learning models   

* Reflect on ethical implications of applying machine learning to cultural heritage collections and discuss potential mitigation strategies

* Summarise the practical, technical steps involved in undertaking machine learning projects

* Identify additional resources on AI and Machine Learning in GLAM

## Contributing

We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any

questions, concerns, or experience any difficulties along the way.

We'd like to ask you to familiarize yourself with our [Contribution Guide](CONTRIBUTING.md) and have a look at

the [more detailed guidelines][lesson-example] on proper formatting, ways to render the lesson locally, and even

how to write new episodes.

Please see the current list of [issues](https://github.com/carpentries-incubator/machine-learning-librarians-archivists/issues) for ideas for contributing to this

repository. For making your contribution, we use the GitHub flow, which is

nicely explained in the chapter [Contributing to a Project](http://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) in Pro Git

by Scott Chacon.

Look for the tag ![good_first_issue](https://img.shields.io/badge/-good%20first%20issue-gold.svg). This indicates that the maintainers will welcome a pull request fixing this issue.

## Maintainer(s)

Current maintainers of this lesson are

* Mark Bell

* Nora McGregor

* Daniel van Strien 

* Mike Trizna

## Authors

A list of contributors to the lesson can be found in [AUTHORS](AUTHORS)

## Citation

To cite this lesson, please consult with [CITATION](CITATION)

[cdh]: https://cdh.carpentries.org

[change-default-branch]: https://docs.github.com/en/github/administering-a-repository/changing-the-default-branch

[community-lessons]: https://carpentries.org/community-lessons

[lesson-example]: https://carpentries.github.io/lesson-example