Jupytext: A tool for Jupyter notebook version control with kdb+/q
5 Jun 2019
Following the release last year of JupyterQ, a Jupyter kernel for kdb+, there is increased interest in the use of Jupyter notebooks for code development with kdb+/q. In this article we take a look at Jupytext by developer Marc Wouts, which attempts to solve version control.
Random Forests in kdb+
12 Jul 2018
Using random forest algorithms for machine learning in kdb+ is made easier with embedPy and JupyterQ notebooks. This blog explains how.
Decision Trees in kdb+
5 Jul 2018
An outline of how to implement Decision Trees, effective machine learning algorithms, with kdb+ using JuypterQ notebooks and embedPy.
Classification using K-Nearest Neighbors in kdb+
21 Jun 2018
This blog, in a series about ML and kdb+, gives a demonstration of how to use a JupyterQ notebook to implement K-nearest neighbors in kdb+
Dimensionality Reduction in kdb+
14 Jun 2018
Dimensionality reduction methods have been the focus of much interest within the statistics and machine learning communities for a range of applications. These techniques have a long history as being methods for data pre-processing. Dimensionality reduction is the mapping of data to a lower dimensional space such that uninformative variance in the data is discarded. By doing this we hope to retain only that data that is meaningful to our machine learning problem. In addition, by finding a lower-dimensional representation of a dataset we hope we can improve the efficiency and accuracy of machine learning models.
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.