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 on“Using 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