- 10:45-12:15 Deep Neural Networks with PyTorch, Stefan Otte
- 15:00-16:30 Production ready Data-Science with Python and Luigi, Mark Keinhörster
- 09:00-10:30 Tricks, tips and topics in Text, Analysis Bhargav Srinivasa Desikan
- 10:45-12:15 Leveling up your storytelling and visualization skills, Gerrit Gruben
- 15:00-16:30 Search Relevance: A/B testing to Reinforcement Learning, Arnab Dutta
- 14:15-15:00 Building new NLP solutions with spaCy and Prodigy, Matthew Honnibal
- 15:00-15:45 How I Made My Computer Write it’s First Short Story, Alexander Hendorf
- 11:00-11:45 Five things I learned from turning research papers into industry prototypes, Ellen König
- 15:00-15:45 How to scare a fish (school), Andrej Warkentin
- 14:15-15:00 Manifold Learning and Dimensionality Reduction for Data Visualization and Feature Engineering, Stefan Kühn
- 15:45-16:30 On Laplacian Eigenmaps for Dimensionality Reductio, Juan Orduz
- 10.15-11:00 Industrial ML - Overview of the technologies available to build scalable machine learning, Alejandro Saucedo
- 13:30-14:15 Interfacing R and Python, Andrew Collier
- 14:15-15:00 Extending Pandas using Apache Arrow and Numba, Uwe L. Korn
- 13:30-14:15 All that likelihood with PyMC3, Junpeng Lao
- 16:00-16:45 Battle-hardened advice on efficient data loading for deep learning on videos, Valentin Haenel
- 15:15-16:00 Meaningful histogramming with Physt, Jan Pipek
- Missing talk ;) Some tools to ease EDA, Stefan Otte