Devoxx Belgium 2018
from Monday 12 November to Friday 16 November 2018.
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.
Today in our tutorial session using a case study technique we will have a brief description of Hyperledger Composer, its capabilities, and its role in the modeling and implementing a Blockchain application. Then a closer look at Hyperledger Fabric and a typical Blockchain solution Architecture. After that a description of the elements of a business network, role of channels, and how world state is maintained. Exploring An explanation of how consensus works, endorsement, and ordering work in Hyperledger Fabric operates. Then touch Hyperledger Composer and Hyperledger Fabric security. A description of the different possible deployment options of Blockchain solutions including local, on IBM Container Service, and IBM Blockchain Platform.
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.