Machine learning techniques featured in JupyterQ notebooks

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

Building on these capabilities, the Kx ML team has created a number of JupyterQ notebooks, and continues to develop more. 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.

Our current list of ML notebooks are described in the following Kx blogs:

If you would like to further investigate the uses of embedPy and machine learning algorithms in Kx, keep checking back to the ML notebooks on GitHub. You can use Anaconda to integrate into your Python installation to set up your machine learning environment, or you can build your own, which consists of downloading kdb+, embedPy and JupyterQ. You can find the installation steps on the ML section of the kdb+ Developers’ site.


ML and kdb+

Machine Learning Toolkit Update: Cross-Validation and ML Workflow in kdb+

23 Jul 2019 | , ,

The Kx machine learning team has an ongoing project of periodically releasing useful machine learning libraries and notebooks for kdb+. This release relates to the areas of cross-validation and standardized code distribution procedures for incorporating both Python and q distribution. Such procedures are used in feature creation through the FRESH algorithm and cross-validation within kdb+/q.

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Machine Learning Toolkit Update: Multi-parameter FRESH and updated utilities

25 Apr 2019 | , ,

This latest toolkit release, is the first in a series of planned releases in 2019 that will add updates to the functionality of the FRESH (Feature Extraction based on Scalable Hypothesis tests) algorithm and the addition of a number of accuracy metrics, preprocessing functions and utilities. In conjunction with code changes, modifications to the namespace structure of the toolkit have been made to streamline the code and improve user experience.