Emrah Tasli, Stas Girkin – Deep Learning at Booking.com

Dafne van Kuppevelt – Deep learning for time series made easy

Maciej Kula – Neural Networks for Recommender Systems

Niels Zeilemaker – Deploying Python models to production

Lucas Javier Bernardi – Diagnosing Machine Learning Models

Giovanni Lanzani – Applied Data Science

Maarten Breddels – A billion stars in the Jupyter Notebook

Maxim Lapan – Deep Reinforcement Learning: theory, intuition, code

Rob Romijnders – Using deep learning in natural language processing

Gilles Louppe – Bayesian optimization with Scikit-Optimize

|Carsten van Weelden, Beata Nyari – Siamese LSTM in Keras: Learning Character-Based Phrase

Rafael Schultze Kraft – Data Science in the Internet of Things with Python and Spark

Mark Jan Harte – Training a TensorFlow model to detect lung nodules on CT scans

Stephen Helms – Finding Needles in a Growing Haystack

Niels Denissen – A practical guide to speed up your application with Asyncio

Holden Karau – Debugging PySpark - Pretending to make sense of JVM stack traces

Tristan Boudreault – Survival analysis for conversion rates

Ruben Mak – Successfully applying Bayesian statistics to A/B testing in your business

Katharine Jarmul – Keynote: Ethical Machine Learning: Creating Fair Models in an Unjust World

Iain Barr – Pythonic Metal

Nigel Small – A Pythonic Tour of Neo4j and the Cypher Query Language

Cees Taal – Smoothing your data with polynomial fitting: a signal processing perspective

Rogier van der Geer – Risk Analysis

Roelof Pieters – Creativity and AI: Deep Neural Nets "Going Wild"

Dirk Gorissen – Keynote: Python vs Orangutan

John Paton – Simulate your language. ish.

Jakub Hava – Different Strategies of Scaling H2O Machine Learning on Apache Spark

Sylvain Corlay, Johan Mabille – xtensor: the lazy tensor algebra library

Lightning Talks & Closing Remarks