Skip to content

This repository contains slide decks and materials from PyData Warsaw Conference 2018

Notifications You must be signed in to change notification settings

PyDataPoland/PyData-Warsaw-Conference-2018

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

48 Commits
 
 
 
 
 
 
 
 

Repository files navigation

PyData Warsaw Conference 2018

Links

Slides and materials

Feel invited to Pull Request with link to slides or the project website.

Keynote

  • Aleksandra Przegalińska - "Trust in the device dimensions of human chatbot relations"
  • Lynn Cherny - Tl;dr: summarization
  • Stefania Druga - "Cognimates: Read, Write and Tinker with AI"
  • Gene Kogan - "The Neural Aesthetic"

Talks

Monday Nov. 19, 2018

  • "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
  • "Can you trust neural networks?" - Mateusz Opala
  • "From Data to Deliverable" - Steph Samson
  • "Overview of imbalanced data prediction methods" - Robert Kostrzewski
  • "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
  • "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
  • "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 Lew
  • "Spot the difference: train your image analytics model to recognize fine grained similarity" - Katarina Milosevic, Ioana Gherman,
  • In Browser AI - neural networks for everyone - Kamila Stepniowska, Piotr Migdał
  • "Using convolutional neural networks to analyze bacteriophages DNA" - Michał Jadczuk
  • "Comixify: Turning videos into comics" - Adam Svystun, Maciej Pęśko,
  • "High Performance Data Processing in Python" - Donald Whyte
  • What ad is this? - Adam Witkowski
  • Spammers vs. Data: My everyday fight - Juan De Dios Santos
  • "Pragmatic application of Machine Learning in commercial products" - Łukasz Słabiński

Tuesday Nov. 20, 2018

  • "Towards Data Pipeline Hyperparameter Optimization" - Alex Quemy
  • "Similarity learning using deep neural networks" - Jacek Komorowski
  • "Application of Recurrent Neural Networks to innovative drug design" - Rafał A. Bachorz
  • "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
  • "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
  • "Uncertainty estimation and Bayesian Neural Networks" - Marcin Możejko
  • "A deep revolution in speech processing and analysis" - Pawel Cyrta15:30
  • "Predicting preterm birth with convolutional neural nets" - Tomasz Włodarczyk
  • "Can you enhance that? Single Image Super Resolution" - Katarzyna Kańska
  • "Burger Quest: finding the best hamburger in town!" - Roel Bertens
  • "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

Tutorials

  • "Building Interactive Dashboards in Python - First steps with Dash" - Mikolaj Olszewski
  • "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
  • "Peltarion: Build Deep Neural Networks without all the Overhead" - Justin Shenk
  • "Structuring machine learning models by using pipelines" - Paweł Jankiewicz
  • "Serverless Approach to Working with Data" - Jakub Nowacki
  • "Introduction to Recommendation Systems" - Piotr Bigaj, Jakub Gasiewski, Przemek Kepczynski

About

This repository contains slide decks and materials from PyData Warsaw Conference 2018

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages