PyData Berlin 2017
June 30th to July 2nd
Diversity Program
PyData is community of users and developers of python data tools. We firmly believe that the community becomes better with each new member. We welcome new member from diverse backgrounds and seek to actively aid people, especially people new to community, to enjoy their time at PyData.
In light of that PyData runs a diversity program to encourage new speakers by offering mentorship on talk and conference prepartion. The diversity program also includes limited number of scholarships for people who might not otherwise be aable to attend the conference. In 2017 we were able offer free or reduced priced tickets as well as travel reimbursements to 20 scholarships awardees. Focusing our efforts on the local community within the EU, allowed us to maximise the number of people we could support. The scholarship was 100% supported by sponsors and this year we received a generous donation from the Python Software Foundation, who have been a consistent supporter of PyData conferences around the world.
You can read about Szilvia Téglás’ and Katinka Jeszenszki’s reflections on their first PyData here.
Impressions from the Conference
Talks
Ethics in Machine Learning Panel
Ethics in Machine Learning Panel Q&A
The Future of Cybersecurity Needs You, Here is Why.
Keynote Q&A
Are many of your worries about AI wrong?
Keynote Q&A
Natural Language Processing: Challenges and Next Frontiers
Keynote Q&A
Data Science & Data Visualization in Python
Best Practices for Debugging
Engage the Hyper-Python
Where are we looking? Predicting human gaze using deep networks.
Text similiarity with the next generation of word embeddings in Gensim
Analysing user comments with Doc2Vec and Machine Learning classification
Compositional distributional semantics for modelling natural language
Conversational AI: Building clever chatbots
Semi-Supervised Bootstrapping of Relationship Extractors
Is That a Duplicate Quora Question?
Developments in Test-Driven Data Analysis
Evaluating Topic Models
A word is worth a thousand pictures: Convolutional methods for text
Gold standard data: lessons from the trenches
The path between developing and serving machine learning models.
Advanced Metaphors in Coding with Python
Leveling up your Jupyter notebook skills
Introduction to Julia for Scientific Computing and Data Science
Pandas from the Inside / "Big Pandas"
Introduction to Data-Analysis with Pandas
Topic Modelling (and more) with NLP framework Gensim
Social Networks and Protest Participation: Evidence from 130 Million Twitter Users
Data Science for Digital Humanities: Extracting meaning from Images and Text
AI assisted creativity
Spying on my Network for a Day: Data Analysis for Networks
Biases are bugs: algorithm fairness and machine learning ethics
Fairness and transparency in machine learning: Tools and techniques
Deep Learning for detection on a phone
Towards Pythonic Innovation in Recommender Systems
“Which car fits my life?” - mobile.de’s approach to recommendations
On Bandits, Bayes, and swipes: gamification of search
Machine Learning to moderate ads
Large Scale Vandalism Detection in Knowledge Bases
Open Data Use Cases
Clean Code in Jupyter notebooks, using Python
Fast Multidimensional Signal Processing using Julia with Shearlab.jl
Polynomial Chaos: A technique for modeling uncertainty
TNaaS - Tech Names as a Service
Finding Lane Lines for Self Driving Cars
Kraft - Building smart IoT applications with Python and Spark
Introduction to Search
Patterns for Collaboration between Data Scientists And Software Engineers
Kickstarting projects with Cookiecutter
Patsy: The Lingua Franca to and from R
fai Chow - Introduction to Machine Learning with H2O and Python
Introductory tutorial on data exploration and statistical models
Lightning Talks
Blockchains for Artificial Intelligence