We are so glad you're thinking about contributing to the Keep-Current project! If you're unsure about something, please don't hesitate to ask us.
We want to ensure a welcoming environment for all the Keep-Current different repositories. Please follow the Code of Conduct.
We encourage you to read the License and the ReadMe.
All contributions to this project will be released under the Apache License. By submitting a pull request, you are agreeing to comply with this waiver of copyright interest.
We welcome anyone who would like to join and contribute. We meet regularly every month in Vienna through the Data Science Cafe meetup of the VDSG, show our progress and discuss the next steps. Please follow our meetup page to get updates regarding our next meeting.
This repository is dedicated to machine-learning based engine. Among the items we handle here are document classifications, semantic representations of documents, topics distance mapping, adaptation according to user's feedback, etc.
If you're new to Machine Learning, we suggest starting by reading the following sources:
We lean heavily on existing tools as well as developing our own new methods. We are colaborating through Google Colab notebooks. Among the existing tools we are using:
We want to use this goal also as a reason to have a playground to implement, test, hack and compare different models.
Currently, we're working on:
- Collecting data to create a 'gold-truth' dataset, to be able to compare different models on.
- Developing unsupervised models for document clustering
If you wish to assist in different aspects, such as Data Engineering, Web development, DevOps, we have divided the project to several additional repositories focusing on these topics.