Conference Schedule

View past PyData event schedules here.

Tutorial Sessions — Wednesday Oct. 18, 2017

  Track 1 Track 2 Track 3
9:30

Registration and Coffee

10:00 Anomaly Detection in web application Part 1 Damian Gajewski, Piotr Bigaj Spark lesson 2 for Data Scientists Part 1 Jakub Nowacki TBA -
12:00

Lunch

12:45 Anomaly Detection in web application Part 2 Damian Gajewski, Piotr Bigaj Spark lesson 2 for Data Scientists Part 2 Jakub Nowacki Getting Started with Robotics using ROS(Robotics Operating System) and Python Ridhwan Luthra
14:45

Coffee Break

15:15 Anomaly Detection in web application Part 3 Damian Gajewski, Piotr Bigaj Introduction to Data-Analysis with Pandas Alexander Hendorf TBA Mateusz Opala
17:15

General Sessions — Thursday Oct. 19, 2017

  Track 1 Track 2 Track 3
9:00

Registration and Coffee

9:45

Opening Notes

10:00 TBA Anthony Gulli
10:45 Towards Interpretable Accountable Models Katharine Jarmul
11:30

Coffee Break

12:00 Teaching Machine Learning Piotr Migdał 10 things that you really should know about jupyter notebooks Jakub Czakon TensorTraffic - traffic prediction using machine learning Paweł Gora
12:30

Technical Break

12:35 Recent advances in neural machine translation Marcin Chochowski Image classification with Tensorflow and Google Cloud Machine Learning Michal Brys Learning to solve Rubik's cube Szymon Matejczyk
13:05

Technical Break

13:10 Natural Language Processing for the Impatient Sebastian Dziadzio First steps with Julia for numerical computing Bogumił Kamiński Building a Gesture Recognition System using Deep Learning Joanna Materzynska
13:40

Lunch

14:40 Natural Language Processing challenges in the context of Polish and other Slavic languages Łukasz Kobyliński, Michał Wasiluk Maxing out supervised learning with model-based hyperparameter selection Artur Suchwałko, Tomasz Melcer Use of vectorized text and siamese recurrent neural networks for Allegro offers clustering Mikołaj Sędek, Rafał Wojdan
15:10

Technical Break

15:15 Understanding text Przemyslaw Chojecki Can one do better than XGBoost? Presenting 2 new gradient boosting libraries - LightGBM and Catboost Mateusz Susik Mining articles for practical inisghts for content creation Łukasz Dziekan /CTO @ Finai
15:45

Technical Break

15:50 Image generation using deep learning Michał Jamroż Advances in 2D/3D image segmentation using CNNs - a complete solution in a single Jupyter notebook Krzysztof Kotowski Deep neural networks in social media content analysis Adam Bielski
16:20

Coffee Break

16:50 Despicable machines: how computers can be assholes Maciej Gryka The Python Ecosystem for Data Science: A Guided Tour Christian Staudt Neural Translation of Musical Style Iman Malik
17:20

Technical Break

17:25 Debugging machine learning, Mostly for profit, but with a bit of fun too Michał Łopuszyński You are using the wrong database! Szymon Warda Why does my girlfriend dislike my music? - a look at my music using machine learning and statistics Juan De Dios Santos Rivera
17:55

Technical Break

18:00 Playground of Evol Adventures Rogier van der Geer, Vincent D. Warmerdam Safer roads with kafka&python. Mariusz Strzelecki Saving Santa's back with Python and data Jelte Hoekstra
18:30

Closing Notes

18:40

 

19:00

Afterparty

23:59

General Sessions — Friday Oct. 20, 2017

  Track 1 Track 2 Track 3
9:20

Morning Coffee

9:50

Opening Notes

10:00 Winning together: Bridging the gap between academia and industry (Python edition) Radim Řehůřek
10:45 Building People Analytics Cameron Davidson-Pilon
11:30

Coffee Break

12:00 From a model to production like a Pro: Software-engineering Best-Practices Marcel Krčah PyTorch: a framework for research of dynamic deep learning models. How, why, and what's next? Adam Paszke Protein biophysics from Machine Learning perspective Kamil Tamiola
12:30

Technical Break

12:35 Data-driven and Test-driven product development with Airflow, Jupyter and (Py)Spark at Allegro Tomasz Bartczak Neural Networks Activation functions overview Dominik Lewy Analyzing GitHub, how developers change programming languages over time Waren Long
13:05

Technical Break

13:10 Developing Data Science products - Agile approach at Grupa Pracuj Jan Zyśko, Magdalena Kalbarczyk How to visualize neural network parameters and activity Justin Shenk Masking personal data in medical documents Kornel Lewandowski
13:40

Lunch

14:40 Feature importance and ensemble methods : understanding your prediction with your variables Constant Bridon Exploring word2vec vector space Julia Bazińska Analysing flight safety data with Python Jesús Martos, Alejandro Saez Mollejo
15:10

Technical Break

15:15 Data science making real money: a selection of interesting use cases. Michał Kudelski Virtual environments and dependency management in Python Piotr Grzesik Python for Multi-Family Real Estate Investing: Data-Driven Apartment Rent Pricing Corey J. Gallon
15:45

Coffee Break

16:15 Conduct a computational experiment in 10 minutes (instead of 1 week) & process results in real-time Laurent Picard Useful Decorators for Data Science Uri Goren Sport analysis with Python ThuyLe
16:45

Technical Break

16:50

Ignite Talks

17:50

Closing Notes

18:00