Thanks to Watson, you will see what it can be like to be a bridge crew member on a starship in Ubisoft's new game.
Continue reading Star Trek: Bridge Crew now live with IBM Watson Speech Technology
Developers, meet the Watson IoT Edge Analytics SDK, a tool that lets you move the analytics processing closer to the device that sends the data.
Unveiling a Watson-powered earpiece that accurately translates spoken conversation and written text across eight of the world's most widely spoken languages within 3 to 5...
Continue reading developerWorks newsblast: The Watson universal translator
Romeo Kienzler explains the importance of feature engineering when doing classifications.
Continue reading How feature engineering increases prediction performance in classification
In this presentation from the 2016 Swiss Data Conference, Romeo Kienzler introduces DeepLearning4J and explains how it can be parallelized for ApacheSpark.
Continue reading Introduction to DeepLearning4J on ApacheSpark
In this webcast, Romeo Kienzler demonstrates an algorithm that detects anomalous values in a time series by analyzing the "moving zscore."
Continue reading Moving zscore-based anomaly detector on the IoT Edge using NodeRED
Romeo Kienzler discusses data clustering with reference to IoT vibration accelerometer sensor data.
Continue reading Introduction to Clustering with IoT vibration accelerometer sensor data
Romeo Kienzler chats with Berthold Reinwald of IBM Almaden Research Center in San Jose, CA. The two discuss SystemML, for which Berthold is technical lead.
Continue reading Interview with Berthold Reinwald, the brain behind Apache SystemML
In this presentation to the 2017 Data Works Summit in Munich, Romeo Kienzler discusses strategies for parallelization of DeepLearning neural networks.
Continue reading Parallelization strategies of DeepLearning neural networks
In this presentation, Romeo Kienzler discusses his autoencoder application used on IoT data. Following Romeo's talk is an advanced presentation on neural networks by...
Continue reading Autoencoder on IoT Data with Theano+Lasagne and deep learning