Romeo Kienzler
Romeo Kienzler is Chief Data Scientist and DeepLearning/AI Engineer at IBM Watson IoT and as IBM Certified Senior Architect he helps clients worldwide to solve their data analysis challenges.
He holds an M. Sc. (ETH) in Computer Science with specialisation in Information Systems, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology Zurich.
He works as an Associate Professor for artificial intelligence at a Swiss University and his current research focus is on cloud-scale machine learning and deep learning using open source technologies including TensorFlow, Keras, DeepLearning4J, Apache SystemML and the Apache Spark stack.
He also contributes to various open source projects. He regularly speaks at international conferences including significant publications in the area of data mining, machine learning and Blockchain technologies.
Romeo is lead instructor of the Advance Data Science specialisation on Courera https://www.coursera.org/
Recently his latest book on Mastering Apache Spark V2.X has been published: http://amzn.to/2vUHkGl
Romeo Kienzler is a member of the IBM Technical Expert Council and the IBM Academy of Technology - IBM’s leading brain trusts. #ibmaot
Subscribe to my mailling list for getting occasional technical information on AI, ML, BlockChain, IoT and Quantum Computing http://nopanic.ai/list
Twitter: @RomeoKienzler
Web: www.ibm.com
DeepLearning for Developers - Introduction to DeepLearning with Keras and TensorFlow
TensorFlow is an awesome library. But for the average developer fiddling with linear algebra is far to complicated. In this talk we'll give you a fast track recipe to master DeepLearning challenges using the Keras framework on top of TensorFlow. We'll start with basic image classification, show how you can implement a chat- bot and end with a Cryptocurrency price predictor. At the end of this talk you'll know how Convolutional Neural Networks, Long-Short-Term Memory Networks and Autoencoders work and how you can apply them using Keras and TensorFlow.