Introduction to deep learning

This lesson gives an introduction to deep learning.

Lesson Design

The design of this lesson can be found in the lesson design

Target Audience

The main audience of this carpentry lesson is PhD students that have little to no experience with deep learning. In addition, we expect them to know basics of statistics and machine learning.

Lesson development sprints

We regularly host lesson development sprints, in which we work together at the lesson. The next one is scheduled for the 22nd and 23rd of July 2024. We kickoff with an online meeting at 10:00 CEST. If you want to join (you are very welcome to join even if you have never contributed so far) send an email to deep-learning-lesson-dev@esciencecenter.nl .

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 and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

Please see the current list of issues for ideas for contributing to this repository.

Please also familiarize yourself with the lesson design

For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag . This indicates that the maintainers will welcome a pull request fixing this issue.

Teaching this lesson?

We would be very grateful if you can provide us with feedback on this lesson. You can read more about hosting a lesson pilot for an incubator lesson here.

You can notify us that you plan to teach this lesson by creating an issue in this repository or posting a message in the carpentries Slack Machine Learning channel. Please note the questions below to get an indication of the sort of feedback we expect.

After teaching the lesson, please create an issue (or comment on the issue you created before teaching) with general feedback on teaching the lesson. As a template, you can use the following questions:

• How much time did you need for the material? (preferably per episode)
• How much time did you need for the exercises?
• Where there any technical issues that arose during setup?
• Where there any bugs or parts of the lesson code that did not work as expected?
• Where there any incorrect or missing exercise solutions?
• Which parts of the lesson were confusing for learners?
• Which questions did learners ask?

In addition, you are very welcome to add issues or pull requests that address more specific feedback.

Setup the Workshop Website locally

To build this lesson locally, you should follow the setup instructions for the workbench. In short, make sure you have R, Git, and Pandoc installed, open R and use the following commands to install/update the packages needed for the infrastructure:

# register the repositories for The Carpentries and CRAN
options(repos = c(
  carpentries = "https://carpentries.r-universe.dev/",
  CRAN = "https://cran.rstudio.com/"
))

# Install the template packages to your R library
install.packages(c("sandpaper", "varnish", "pegboard", "tinkr"))

Rendering the website locally

See the Carpentries Workbench usage instructions on how to render the website locally.

Maintainer(s)

Current maintainers of this lesson are

• Peter Steinbach
• Colin Sauze
• Djura Smits
• Sven van der Burg
• Pranav Chandramouli
• Carsten Schnober

Citation and authors

To cite this lesson, please consult with CITATION.cff. This also holds a list of contributors to the lesson.