Machine learning techniques featured in JupyterQ notebooks

19 Jul 2018 | , , , , , , , ,

Machine learning with kdb+ has been a theme of the Kx blog over the past couple of months because of the release of a series of JupyterQ notebooks on the Kx ML GitHub. As more different kinds of developers work with ML techniques, the uses for kdb+ in ML applications is growing. The release of embedPy, which loads Python into kdb+, so Python variables and objects become q variables and either language can act upon them, has been a catalyst for this trend. With embedPy, Python code and files can be embedded within q code, and Python functions can be called as q functions.

Natural Language Processing in kdb+

30 May 2018 | , , , , , , ,

As part of Kx25, the international kdb+ user conference held May 18th, a series of seven JuypterQ notebooks were released and are now available on Each notebook demonstrates how to implement different machine learning techniques in kdb+, primarily using embedPy, to solve all kinds of machine learning problems, from feature extraction to fitting and testing a model. These notebooks act as a foundation to our users, allowing them to manipulate the code and get access to the exciting world of machine learning within Kx.

Natural Language Processing in q

12 Dec 2016 | ,

Expert kdb+ programmer Ben Jeffery of Kx labs recently presented his NLP in Q talk at the Kx Community NYC Meetup. When Ben began this project he found there were no existing NLP libraries in q. He decided to focus on vector operations because q is so suitable for these, rather than named entity recognition, part-of-speech tagging and coreference resolution. In his talk, Ben demonstrated clustering, finding groupings of entities in documents, like terms and proper nouns, as well as showing other features of NLP analytics in q. His examples included the Old and New Testaments of the Bible, Moby Dick and Jeff Skillings emails from his Enron days. Using his NLP program, Ben made easy work of...