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
PyData Amsterdam 2017
29 Talks