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.

Neural Networks in kdb+

7 Jun 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 https://code.kx.com/q/ml/. 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.

Using q in Machine Learning with Neural Network and Clustering Examples

4 Apr 2017 | , , ,

Tokyo-based kdb+ programmer, and algorithmic quantitative analyst, Mark Lefevre recently gave a couple of talks about using high-performance machine learning with kdb+ at the Kx Community Tokyo Meetup. His talk “Using Q to Read Japanese” focused on utilizing neural networks and how supervised learning can be used in q to teach a machine to recognize Japanese characters from handwritten images. His second talk, “Kx for Wine Tasting” focused on utilizing the k-means clustering algorithm and unsupervised learning in q to teach a machine to appreciate wine!