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contributing panda

Thank you for wanting to contribute! Open Curriculai would love your help to improve its coverage, quality and usefulness. We value contributions both to the Curriculum and to the Resource Hub. You can contribute by either sending a pull request, or creating an issue.

If you're new to Github , read these two links - pull requests, issues - to understand how to create them, and what their differences are.

Please use pull requests for minor changes such a typos, spelling errors, a missing URL link, etc.

Submit an issue if you would like to:

  • 📄 Propose material to be included in the Curriculum or in the Resource Hub.
  • 💡 Suggest an idea about how to improve the project.
  • 👎 Voice any criticism you have about the site.

You are invited to read and participate to issues and pull requests by responding to proposals and comments.

Create a Pull Request Create an issue

Contributing Guidelines

Guidelines for Contributing to the Curriculum

Curriculum resources should span entire subjects and are not abound to a specific format. However, if they are videos or blogposts, they should be a series that covers a whole topic.

For the purposes of contributing to the curriculum, it is useful to think of a resource in 4 dimensions:

  1. Topic - Ex: Linear Algebra, learning Python.
  2. Format - Ex: a course, a book, a video series.
  3. Difficulty - Is it catered towards absolute beginners or more towards people with existing experience?
  4. Theory vs practicality - Fastai's Practical Deep Learning for Coders is considered, as its named suggests, very practical. For instance, in lecture 1, they already teach you how to create your own dataset and train an image classifier using their library. In lecture 2, you learn how to put it in production.

In general, the curriculum is built in a way that dimensions 1-4 don't overlap with other resources. For example, there is only 1 beginner course on learning Python. Both Deep Learning Specialization & Practical Deep Learning for Coders are introductions to deep learning, but the former will expose more math and theory while the other is more practical. There is also an introductory deep learning book recommendation because the two previous examples were courses.

With that in mind, when you are submitting a resource, ask yourself the question if your resource overlaps or not. If not, is it better than the existing one? Why do you think that is the case?

Another aspect of the curriculum is to include tips and recommendations as you're progressing such as, "Find a study group", or "participate in a Kaggle competition". If you have personal tips you would like to share that aren't mentioned, feel free to submit an issue.

Guidelines for Contributing to the Resource Hub

Generally, the scope of the content pertaining to the Resource Hub is much smaller than those in the curriculum. You will find that most links are single blogposts or videos that help elucidate a topic and present it in a unique and special way. Feel free to also suggest entire lectures or books as well if you think they'll complement the curriculum content well. Add a justification for why in your request.

If a topic is missing from the Resource Hub, ensure that the quality of your proposition is high and that is has truly helped you learn a concept. Please explain how that is the case in your request. If the topic is already covered, ask yourself what is unique about the way a concept is being explained or in what innovative way is it being presented.