The Incubator Lesson Spotlight highlights a lesson under development by our community in The Carpentries Incubator. In this edition, we look at the progress being made on the Introduction to Deep Learning lesson, and hear from the authors about how The Carpentries community can get involved with the ongoing development of this lesson.
- Title: Introduction to Deep Learning
- Lesson Pages: https://carpentries-incubator.github.io/deep-learning-intro
- Lesson Repository: https://github.com/carpentries-incubator/deep-learning-intro
- Prepare input data for use for deep learning
- Design and train a Deep Neural Network
- Troubleshoot the learning process
- Measure the performance of the network
- Visualize data and results
The main audience of this lesson is considered to have an academic background at any level. More importantly, we expect them to know basics of statistics and machine learning to follow through with the material.
The lesson is currently in beta stage, which means the main content is there but there is room for fine tuning. It has been taught several times by the lesson authors, and a few times by others. We work actively on addressing issues and pull requests from beta teachers.
How You Can Help Develop This Lesson
As we entered the beta stage, we are currently looking for volunteers to test this lesson and provide feedback.
Feedback on the lesson material can be posted as an GitHub issue, you are also very welcome to create a pull request with suggested improvements.
About This Series
Incubator Lesson Spotlight is a regular feature in The Carpentries blog and Carpentries Clippings newsletter, highlighting the great work our community is doing to develop new lessons in The Carpentries Incubator. Developers of any lesson in the Incubator are encouraged to submit details about their material for inclusion in the series. If you would like to increase the visibility of your lesson and encourage Carpentries community members to contribute to its ongoing development, fill in this short form.
Dialogue & Discussion
Comments must follow our Code of Conduct.