- 09:00-09:30 Using GANs to improve generalization in a semi-supervised setting - trying it in open datasets, Andreas Merentitis, Carmine Paolino, Vaibhav Singh
- 10:45-12:15 Deep Neural Networks with PyTorch, Stefan Otte
- 13:15-14:45 Scaling and reproducing deep learning on Kubernetes with Polyaxon, Mourad Mourafiq
- 15:00-16:30 Production ready Data-Science with Python and Luigi, Mark Keinhörster
- 16:45-18:15 Deploying a machine learning model to the cloud using AWS Lambda, Benjamin Weigel
- 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
- 13:15-14:45 A Hands-On Introduction to Your First Data Science Project Em Grasmeder, Jin Yang
- 15:00-16:30 Search Relevance: A/B testing to Reinforcement Learning, Arnab Dutta
- 16:45-18:15 Deprecating the state machine: building conversational AI with the Rasa stack Justina Petraitytė
- 11:00-11:45 Visual concept learning from few images, Vaibhav Singh
- 11:45-12:30 Simple diagrams of convoluted neural networks, Piotr Migdał
- 11:00-11:45 Five things I learned from turning research papers into industry prototypes, Ellen König
- 11:45-12:30 Spatial Data Analysis With Python, Dillon R. Gardner
- 12:30-13:15 Python Unittesting for Ethereum Smart Contracts or how not to create your own Cryptocurrency, Robert Meyer
- 11:45-12:30 Towards automating machine learning: benchmarking tools for hyperparameter tuning, Thorben Jensen
- 12:30-13:15 Launch Jupyter to the Cloud: an example of using Docker and Terraform, Cheuk Ting Ho
- 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
- 15:45-16:30 Understanding and Applying Self-Attention for NLP, Ivan Bilan
- 14:15-15:00 Python in Medicine: analysing data from mechanical ventilators and patient monitors Gusztav Belteki
- 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
- 15:00-15:45 Extracting relevant Metrics with Spectral Clustering, Evelyn Trautmann
- 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
- 10:15-11:00 GDPR in practise - Developing models with transparency and privacy in mind, Łukasz Mokrzycki
- 11:00:11:45 Privacy-preserving Data Sharing, Omar Ali Fdal
- 11:45-12:30 pyGAM: balancing interpretability and predictive power using Generalized Additive Models in Python Dani Servén Marín
- 10:15-11:00 When to go deep in Computer Vision… and how, Irina Vidal Migallón
- 11:45-12:30 The Face of Nanomaterials: Insightful Classification Using Deep Learning Angelo Ziletti
- 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
- 15:15-16:00 Big Data Systems Performance: The Little Shop of Horrors, Jens Dittrich
- 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
- 16:00-16:45 Practical examples of interactive visualizations in JupyterLab with Pixi.js and Jupyter Widgets, Jeremy Tuloup
- Missing talk ;) Some tools to ease EDA, Stefan Otte