Conference Schedule

View past PyData event schedules here.

Tutorial Sessions — Friday July 6, 2018

  Track 1 Track 2
8:00

Registration & Breakfast

9:00 Using GANs to improve generalization in a semi-supervised setting - trying it in open datasets Andreas Merentitis, Carmine Paolino, Vaibhav Singh Tricks, tips and topics in Text Analysis Bhargav Srinivasa Desikan
10:30

Break

10:45 Deep Neural Networks with PyTorch Stefan Otte Leveling up your storytelling and visualization skills Gerrit Gruben
12:15

Lunch

13:15 Scaling and reproducing deep learning on Kubernetes with Polyaxon Mourad Mourafiq A Hands-On Introduction to Your First Data Science Project Em Grasmeder, Jin Yang
14:45

Coffee

15:00 Deploying a machine learning model to the cloud using AWS Lambda Dr. Benjamin Weigel All that likelihood with PyMC3 Junpeng Lao
16:30 Deprecating the state machine: building conversational AI with the Rasa stack Alan Nichol

 

18:00

General Sessions — Saturday July 7, 2018

  Audimax Hörsaal 3 Kursraum 1 Kursraum 3
8:30

Registration & Breakfast

9:30

Opening Notes

9:45

Keynote

10:45

Neural Networks

Explainability and Privacy

Python Applications

Education

10:45 Deep Neural Networks for Double Dummy at Bridge Lorand Dali GDPR in practise - Developing models with transparency and privacy in mind Łukasz Mokrzycki Python Unittesting for Ethereum Smart Contracts or how not to create your own Cryptocurrency Robert Meyer

Keynote QA

11:30 Visual concept learning from few images Vaibhav Singh Privacy-preserving Data Sharing Omar Ali Fdal Spatial Data Analysis With Python Dillon R. Gardner, PhD

 

12:15 Towards automating machine learning: benchmarking tools for hyperparameter tuning Dr. Thorben Jensen pyGAM: balancing interpretability and predictive power using Generalized Additive Models in Python Dani Servén Marín LightFields.jl: Fast 3D image reconstruction for VR applications Hector Andrade Loarca
13:00

Lunch

14:00

Algorithms

Computer Vision & CNNs

NLP & Sentiment Analysis

Unsupervised Learning & Visualization

14:00 TBA James Powell When to go deep in Computer Vision... and how Irina Vidal Migallón Manifold Learning and Dimensionality Reduction for Data Visualization and Feature Engineering Stefan Kühn
14:45 A/B testing at Zalando: concepts and tools Shan Huang, Grigory Bordyugov Object detection to Instance segmentation: Learn to apply several algorithms along the way Sujatha Subramanian ML and populism Limor Gultchin Extracting relevant metrics with Spectral Clustering Evelyn Trautmann
15:30 Solving very simple substitution ciphers algorithmically Stephen Enright-Ward The Face of Nanomaterials: Insightful Classification Using Deep Learning Angelo Ziletti Where NLP and psychology meet Alexandra Klochko On Laplacian Eigenmaps for Dimensionality Reduction Juan Orduz
16:15

Coffee

16:30

Keynote

17:30

Lightning Talks

18:15

General Sessions — Sunday July 8, 2018

  Audimax Hörsaal 3 Kursraum 1 Kursraum 3
8:00

Registration & Breakfast

9:00

Keynote

10:00

DevOps

NLP

Python in the Field

 

10:00 Industrial ML - Overview of the technologies available to build scalable machine learning Alejandro Saucedo How I Made My Computer Write it's First Short Story Alexander Hendorf Five things I learned from turning research papers into industry prototypes Ellen König

Keynote QA

10:45 How mobile.de brings Data Science to Production for a Personalized Web Experience Dr. Florian Wilhelm, Dr. Markus Schüler Understanding and Applying Self-Attention for NLP Ivan Bilan Python in Medicine: analysing data from mechanical ventilators and patient monitors Gusztav Belteki

Keynote QA

11:30 Simplifying Training Deep & Serving Learning Models with Big Data in Python using Tensorflow Holden Karau ctparse: a practical parser for natural language time expressions in pure python Dr. Sebastian Mika How to scare a fish (school) Andrej Warkentin

 

12:15

Lunch

13:15

Best Practices

Neural Networks

Extending Python

 

13:15 Going Full Stack with Data Science: Using Technical Readiness Level to Guide Data Science Outcomes Emily Gorcenski Simple diagrams of convoluted neural networks dr Piotr Migdał Interfacing R and Python Andrew Collier

 

14:00 Data versioning in machine learning projects Dmitry Petrov Modern Approaches to Bayesian Learning with Neural Networks Paul J. Rozdeba Extending Pandas using Apache Arrow and Numba Uwe L. Korn

 

14:45

Coffee

15:00

Performance

 

Visualization Tools

 

15:00 Big Data Systems Performance: The Little Shop of Horrors Jens Dittrich CatBoost: Fast Open-Source Gradient Boosting Library For GPU Vasily Ershov Meaningful histogramming with Physt Jan Pipek

 

15:45 Battle-hardened advice on efficient data loading for deep learning on videos. Valentin Haenel Launch Jupyter to the Cloud: an example of using Docker and Terraform Cheuk Ting Ho Practical examples of interactive visualizations in JupyterLab with Pixi.js and Jupyter Widgets Jeremy Tuloup

 

16:30

Lightning Talks & Closing Notes

17:15

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