Romeo Kienzler works as Chief Data Scientist in the IBM Watson IoT World Wide team helping clients to apply advanced machine learning at scale on their data.
His current research focus is on scalable machine learning on Apache Spark. He is contributor to ApacheFlink, DeepLearning4J and Apache SystemML by accelerating computations on GPUs. Romeo is a member of the IBM Academy of Technology.

Twitter: #ibmaot

LOCATION: Zürich
KEYWORDS: Concept, Technology, Tools, Hands-On

SPEAKER: Romeo Kienzler   COMPANY: IBM

DeepLearning was the new Hype in 2016 - these algorithms are outperforming the current state-of-the-art in machine learning.

Formerly, with Theano and Tensorflow those frameworks in that space have been limited to the python community. But now with DeepLearning4J we have a powerful, industry ready and scalable DeepLearning framework in place ready to use.

In this talk I'll introduce you to the basic concepts of DeepLearning and the DeepLearning4J Framework. We'll conclude with a hands-on demo where you can detect anomalies in real-time on IoT Sensor data from an Industry 4.0 Predictive Maintenance and Quality use case in order to predict failure on bearings.

LANGUAGE: Talk: en / Slides: en

Romeo Kienzler works as Chief Data Scientist in the IBM Watson IoT World Wide team helping clients to apply advanced machine learning at scale on their data.
His current research focus is on scalable machine learning on Apache Spark. He is contributor to ApacheFlink, DeepLearning4J and Apache SystemML by accelerating computations on GPUs. Romeo is a member of the IBM Academy of Technology.

Twitter: #ibmaot

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