machine-learning

Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream \emph{high noise} in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.

Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in …

State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai. Recently I started to contribute to this project.

Easy configuration handling for complex machine learning pipelines

Free deep-learning in the cloud

Intro duction to processing large quantities of data in R

Moving a draft ML application to a real world production environment can be hard. This talk outlines several tools and strategies to do …

Reverse engineering old data pipelines ; ) and analyzing huge quantities of spatial data.

Individual cost based classification model outperforms classical processes.