Using q in Machine Learning with Neural Network and Clustering Examples

4 Apr 2017 | , , ,
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Expert kdb+ programmer, and algorithmic quantitative analyst, Mark Lefevre, who is based in Tokyo, recently gave a couple of talks about using high-performance machine learning with kdb+ at the Kx Community Tokyo Meetup.

His first talk was called  “Using Q to Read Japanese.” It 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. Neural networks are used in a variety of real-world applications including character recognition, object recognition, image compression, asset path prediction, medicine and self-driving vehicles.

Here is Mark’s presentation onUsing Q to Read Japanese:”  Machine Learning in q

His second and most recent talk, “Kx for Wine Tasting,”  focused on utilizing the k-means clustering algorithm and how unsupervised learning can be used in q to teach a machine to appreciate, well, at least recognize, different types of wine! K-means clustering enjoys broad applications in computer vision, vector quantization, marketing, finance and as a pre-processing step for subsequent machine learning algorithms.

Here is Mark’s presentation on “Kx for Wine Tasting: Kx for Wine Tasting


kx and machine learning

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.

Kx extends relationship with NASA Frontier Development Lab and the SETI Institute

Detection of Exoplanets at NASA FDL with kdb+

13 Dec 2018 | , , , ,

Kx data scientist Espe Aguilera explains a NASA FDL mission to improve the accuracy of finding new exoplanets using machine learning models. The data for the project will come from the Transiting Exoplanet Survey Satellite (TESS), which was launched in April 2018, with the objective of discovering new exoplanets in orbit around the brightest stars in the solar neighborhood.