Classification using K-Nearest Neighbors in kdb+

21 Jun 2018 | , , , , , ,

As part of Kx25, the international kdb+ user conference held May 18th in New York City, 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 a different machine learning technique in kdb+, primarily using embedPy, to solve all kinds of machine learning problems, from feature extraction to fitting and testing a model.

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.

Natural Language Processing in kdb+

30 May 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.