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


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By Stephen O’Connor, Chairman of Quantile Quantile Technologies, a Kx Ventures company, was formed in 2015 by a team of experienced professionals with backgrounds in risk management, clearing, quantitative analysis and trading technology. Quantile is headquartered in London, and serves the global community of derivatives users. Quantile is a FinTech company that helps major financial institutions […]