AI basierte Root Cause Analyse von CPD Störquellen in Docsis Netzen
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.
May 10, 2022
Identifying the root cause of cable network problems with machine learning
Mar 14, 2022
tsai
State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai. Recently I started to contribute to this project.
Sep 12, 2021
ML project configuration management
Easy configuration handling for complex machine learning pipelines
May 8, 2021
fast AI deep learning course on google collab
Free deep-learning in the cloud
Feb 21, 2018
R for HPC and big data
https://docs.google.com/presentation/d/1VgocCul4ZyLVDqqr6iXsBRmmwsGhJkZj6-lzgdXs0ag/edit?usp=sharing A similar talk was held at a Meetup in December 2018 in Vienna using the following slides and focusing on sparklyR: https://docs.google.com/presentation/d/1NHG7-WoEUsjrdxFjy01OmZjxWB-FZomhfrxO-QapzKg/edit?usp=sharing There is even a videom from the talk. It was one of my first video recorded talks ;)
Sep 27, 2017
Make your ML app rock
Dec 10, 2016
Predictive credit scoring
Individual cost based classification model outperforms classical processes.
Apr 27, 2016
Datalake for the enterprise & large geospatial data
Reverse engineering old data pipelines ; ) and analyzing huge quantities of spatial data.
Apr 27, 2016