Conference Schedule

Tutorials have separate tickets available on Eventbrite. Conference ticket does not include Tutorials.

Tutorials (Track 1 and Track 2) planned for Sunday, 18.11, will be organised in Sages office, ul. Nowogrodzka 62c ("48" on intercom, 2nd floor)
Tutorial planned for Wednesday, 21.11, will be organised in Startberry, ul. Grochowska 306/308 (2nd floor)

Before coming to Tutorials please install required software listed on the Prerequisites page

Tutorial Sessions — Sunday Nov. 18, 2018

  Track 1 Track 2
9:30

Registration & Coffee

10:00 Building Interactive Dashboards in Python - First steps with Dash Mikolaj Olszewski
12:00

Lunch

12:45 Recognize drawings in the browser with Tensorflow.js Karol Majek, Monika Koprowska Playing with CNN using Fashion-MNIST. Classification and what else can be done on it? Rafał Wojdan
14:45

Coffee Break

15:15 Peltarion: Build Deep Neural Networks without all the Overhead Justin Shenk Structuring machine learning models by using pipelines Paweł Jankiewicz
17:15

Coffee Break

17:30 Serverless Approach to Working with Data. Jakub Nowacki
19:30

Tutorial Sessions — Wednesday Nov. 21, 2018

  Startberry, Grochowska 306/308, 2 floor
9:30

Registration & Coffee

10:00 Introduction to Recommendation Systems - part 1 Piotr Bigaj, Jakub Gasiewski, Przemek Kepczynski
12:00

Lunch

12:45 Introduction to Recommendation Systems - part 2 Piotr Bigaj, Jakub Gasiewski, Przemek Kepczynski
14:45

Coffee break

15:15 Introduction to Recommendation Systems - part 3 Piotr Bigaj, Jakub Gasiewski, Przemek Kepczynski
17:15

General Sessions — Monday Nov. 19, 2018

  Main Track Track 2 Track 3
9:00

Registration & Coffee

9:45

Opening Notes

10:00
Aleksandra Przegalińska
10:45
Lynn Cherny
11:30

Coffee Break

12:00 PyTorch 1.0: now and in the future Adam Paszke Deep Learning for 3D World: Point Clouds Marcin Mosiołek Where visual meets textual. Luna - overview. Sylwia Brodacka
12:30

Technical Break

12:35 Can you trust neural networks? Mateusz Opala From Data to Deliverable Steph Samson Overview of imbalanced data prediction methods Robert Kostrzewski
13:05

Technical Break

13:10 Recognizing products from raw text descriptions using “shallow” and “deep” machine learning Tymoteusz Wołodźko, Tomasz Płomiński How I learnt computer vision by playing pool Łukasz Kopeć Distributed deep learning and why you may not need it Jakub Sanojca, Mikuláš Zelinka
13:40

Lunch

15:00 AI meets Art Agata Chęcińska Hand in hand with weak supervision using snorkel Szymon Wojciechowski 3d visualisation in a Jupyter notebook Marcin Kostur, Artur Trzęsiok
15:30

Technical Break

15:35 Deep Learning Semantic Segmentation for Nucleus Detection Dawid Rymarczyk Bit to Qubit: Data in the age of quantum computers. Shahnawaz Ahmed Transfer Learning for Neural Networks Dominik Lewy
16:05

Technical Break

16:10 Spot the difference: train your image analytics model to recognize fine grained similarity Katarina Milosevic, Ioana Gherman, Katarina Milosevic In Browser AI - neural networks for everyone Kamila Stepniowska, Piotr Migdał Using convolutional neural networks to analyze bacteriophages DNA Michał Jadczuk
16:40

Coffee Break

17:10 Comixify: Turning videos into comics Adam Svystun, Maciej Pęśko, Tomasz Trzcinski High Performance Data Processing in Python Donald Whyte What ad is this? Adam Witkowski
17:40

Technical Break

17:45 Spammers vs. Data: My everyday fight Juan De Dios Santos Analysing Russian Troll Tweets data with Python Mia Polovina Pragmatic application of Machine Learning in commercial products. Łukasz Słabiński
18:15

Closing Notes

19:00

Afterparty - Klub Harenda, ul. Krakowskie Przedmieście 4

23:59

General Sessions — Tuesday Nov. 20, 2018

  Main Track Track 2 Track 3
9:20

Morning Coffee

9:50

Opening Notes

10:00 Cognimates: Read, Write and Tinker with AI Stefania Druga
10:45 The Neural Aesthetic Gene Kogan,
11:30

Coffee Break

12:00 Similarity learning using deep neural networks Jacek Komorowski Application of Recurrent Neural Networks to innovative drug design Rafał A. Bachorz
12:30

Technical Break

12:35 Computer vision challenges in drug discovery Dr Maciej Hermanowicz Learning to rank @ allegro.pl Tomasz Bartczak, Ireneusz Gawlik The smart shopping basket: A Case Study with deep learning, Intel Movidius and AWS Marcin Stachowiak, Michal Dura, Piotr Szajowski
13:05

Technical Break

13:10 It is never too much: training deep learning models with more than one modality. Adam Słucki Visualize, Explore and Explain Predictive ML Models Przemyslaw Biecek The Dawn of Mind Reading in Python Krzysztof Kotowski
13:40

Lunch

15:00 Hacking Law - or how python can help analyzing legal systems Maria Turant A deep revolution in speech processing and analysis Pawel Cyrta, Uncertainty estimation and Bayesian Neural Networks Marcin Możejko
15:30

Technical Break

15:35 Predicting preterm birth with convolutional neural nets Tomasz Włodarczyk, Szymon Płotka Can you enhance that? Single Image Super Resolution Katarzyna Kańska Burger Quest: finding the best hamburger in town! Roel Bertens
16:05

Coffee Break

16:35 Hitting the gym: controlling traffic with Reinforcement Learning Steven Nooijen Step by step face swap. Sylwester Brzęczkowski Optimizing Deep Neural Network Layer Topology with Delve Justin Shenk
17:05

Technical Break

17:10

Ignite Talks

18:10

Closing Notes

18:20

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