Google Summer of Code 2020 Projects
Shogun is a mentoring organization for Google Summer of Code 2020. There are two projects this year:
This was our ideas page for GSoC 2020. Have a browse if you're interested in joining the Shogun team in 2021 for a summer full of code, learning, and fun. Be part of our diverse community and join efforts into keeping the project buzzing 🐝
We explicitly encourage female students to apply.
To get a feeling for what GSoC with Shogun is about, see our follow-up blog posts by developers and students. To get a feeling for the Shogun community, check out the blog about our Budapest's hackathon!
First step to get involved: read how to get involved. THE best way to start is to solve entrance tasks. We do not consider students who have not contributed any kind of code in the run up to GSoC. In addition to your "technical application", we have collected some tips for your written application.
The best tip for your application: rather than contacting us and asking what to do, send a patch! 📨
Please do not contact Shogun developers directly, but always use the mailing list, GitHub, or StackOverflow.
See here for the list of participating mentors.
The goal of our community is to recruit new long-term developers. If you can show us that you are likely to become a regular contributor for Shogun (by getting involved early, showing curiosity beyond your project, getting involved in releasing upcoming Shogun versions, answering other users' questions), that will supercharge your chances getting selected and delivering a kick-ass project.
For this year's GSoC, we
- Focus on students: We like to take good care of our students adapting mentoring, as much as it makes sense, to their needs and preferences. It is important for us that there is good interaction between students boosting teamwork. We also like our students blogging to show their work to the world and make them proud of their own achievements (starting from a few lines per week is more than enough!).
- Focus on usability: We want to make Shogun easier to use for end-users, algorithm developers, and other open-source projects. This means working on user-API, documentation, error handling, default parameters, and examples. We want to make it easier for scientist developers to write new algorithms. This means working on modernizing the core framework and the internal API, but also includes adding new interfaces, and working on a plugin-based architecture. Finally, we want to continue on improving the quality of the code-base, in particular algorithms that either crash on corner cases, are slow, or unreliable.
- Focus on applications: Despite having had only limited success, we are still trying to push the idea of accepting projects that use Shogun to solve some real life ML problems in a self-contained project. If you have a cool idea, let us know. Due to challenges in earlier years, we will however set the bars high for application projects. Are you up for the challenge?
In addition to the individual projects, all students will be required to:
- write weekly blog posts
- new this year! Prepare a short presentation (5-10 minutes is enough, longer is good too of course). We will arrange a presentations session with students and mentors towards the end of the project
- peer-review a fellow student's work in the middle and at the end of GSoC
- have a good time web-socializing with the other students
- adding to our example/testing system on a weekly basis, working towards the new Shogun API.
- work on cutting down issue list on a weekly basis
GSoC is a marathon, not a sprint, and we expect good performance over the whole project. This means that you are in daily contact with the community and that you work 40 hours per week (you are paid after all!) We have compiled a list of deliverables that every student will have to satisfy. Finally, we really would like you to stay around after GSoC.
- User experience project
- Detox++
- Distribution: web assembly, amalgamation, packaging
- Inside the black box II
- Web-Tool for Disease Estimation with Shogun
- A Kaggle-style pipeline for supervised learning
- Apply Shogun to the Real World
- Model selection v2.0
- Shogun as a wrapper (Keras, XGBoost, Global optimization)
- Interfaces: Matlab & NodeJS
- Integration with frameworks for model deployments
- Time series!
If you find something that excites you in last year's projects, feel free to contact us and tell us what you would like to work on.