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 Introduction to Anomaly Detection Part 1 Damian Gajewski, Piotr Bigaj Spark lesson 2 for Data Scientists Part 1 Jakub Nowacki Effective visualisation with ggplot2 Piotr Ćwiakowski
12:00

Lunch

12:45 Introduction to Anomaly Detection Part 2 Damian Gajewski, Piotr Bigaj Spark lesson 2 for Data Scientists Part 2 Jakub Nowacki Managing large scale projects in R with R Suite Piotr Chaberski
14:45

Coffee Break

15:15 Introduction to Anomaly Detection Part 3 Damian Gajewski, Piotr Bigaj Introduction to Data-Analysis with Pandas Alexander Hendorf Deep Learning for image generation in practice Mateusz Opala, Michał Jamroż
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 BigData and [email protected] Jarek​ ​Kuśmierek
10:45 Towards Interpretable Accountable Models Katharine Jarmul
11:30

Coffee Break

12:00 10 things that you really should know about jupyter notebooks Jakub Czakon Teaching Machine Learning Piotr Migdał 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

15:00 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
15:30

Technical Break

15:35 How to teach a machine to understand a (mathematical) text? Przemyslaw Chojecki Can one do better than XGBoost? Presenting 2 new gradient boosting libraries - LightGBM and Catboost Mateusz Susik Mining articles for practical insights for content creation Łukasz Dziekan /CTO @ Finai, Michał Stolarczyk
16:05

Technical Break

16:10 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
16:40

Coffee Break

17:10 Debugging machine learning. Mostly for profit, but with a bit of fun too. Michał Łopuszyński The Python Ecosystem for Data Science: A Guided Tour Christian Staudt Why does my girlfriend dislike my music? - a look at my music using machine learning and statistics Juan De Dios Santos Rivera
17:40

Technical Break

17:45 Playground of Evol Adventures Rogier van der Geer, Vincent D. Warmerdam You are using the wrong database! Szymon Warda Saving Santa's back with Python and data Jelte Hoekstra
18:15

Closing Notes

18:20

 

19:00

Afterparty @ Piwnica Pod Harendą (ul. Krakowskie Przedmieście 4/6, https://goo.gl/maps/F7HVmtdkkiH2)

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 Bayesian A/B Testing Marc Garcia
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

15:00 Data science making real money: a selection of interesting use cases. Michał Kudelski Exploring word2vec vector space Julia Bazińska Analysing flight safety data with Python Jesús Martos, Alejandro Saez Mollejo
15:30

Technical Break

15:35 Conduct a computational experiment in 10 minutes (instead of 1 week) & process results in real-time Laurent Picard Virtual environments and dependency management in Python Piotr Grzesik Surprise Talk :D James Powell
16:05

Coffee Break

16:35 Despicable machines: how computers can be assholes Maciej Gryka Safer roads with kafka & python Mariusz Strzelecki Sport analysis with Python ThuyLe
17:05

Technical Break

17:10

Ignite Talks

18:10

Closing Notes

18:20

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