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https://github.com/deeplook/pydata_berlin2016_materials

Collection of pointers to slides and repositories from speakers at PyData Berlin 2016
https://github.com/deeplook/pydata_berlin2016_materials

Last synced: 11 months ago
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Collection of pointers to slides and repositories from speakers at PyData Berlin 2016

Awesome Lists containing this project

README 

          
PyData Berlin 2016 Materials

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Keynotes

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Olivier Grisel, Predictive Modelling with Python



- http://ogrisel.github.io/decks/2016_pydata_berlin/

- https://github.com/ogrisel/docker-distributed



Julia Evans, How to trick a neural network



- http://jvns.ca/blog/2016/05/21/a-few-notes-from-my-pydata-berlin-keynote/



We McKinney, Python Data Ecosystem: Thoughts on Building for the Future



- http://de.slideshare.net/wesm/python-data-ecosystem-thoughts-on-building-for-the-future



Regular

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Daniel Kirsch, Functional Programming in Python



- https://github.com/kirel/functional-python



Trent McConaghy, BigchainDB: a Scalable Blockchain Database, in Python



- https://github.com/bigchaindb/bigchaindb



David Higgins, Introduction to Julia for Python programmers



- https://github.com/daveh19/pydataberlin2016



Katharina Rasch, What every Data Scientist should know about data anonymization



- https://github.com/krasch/presentations/blob/master/pydata_Berlin_2016.pdf



Alexander Sibiryakov, Frontera: open source, large scale web crawling framework



- https://github.com/scrapinghub/frontera



Thomas Reineking, Plumbing in Python: Pipelines for Data Science Applications



- Yamal: Not yet Opensourced



Ryan Henderson, image-match: a python library for searching for similar images in large corpora



- https://github.com/ascribe/image-match



Ian Ozsvald, Statistically Solving Sneezes and Sniffles (a work in progress)



- https://speakerdeck.com/ianozsvald/statistically-solving-sniffles-step-by-step-a-work-in-progress

- http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/



Felix Biessmann, Predicting Political Views From Text



- https://github.com/felixbiessmann/



Jie Bao, ExpAn - A Python Library for A/B Testing Analysis



- https://github.com/zalando/expan

- http://www.slideshare.net/JieBao3/expan-presentation-pydata-berlin-2016



Anne Matthies, Zero-Administration Data Pipelines using AWS Simple Workflow



- https://github.com/babbel/floto



Daniel Moisset, Bridging the gap: from Data Science to service



- https://github.com/machinalis/slides/tree/master/data-science-to-service



Katharine Jarmul, Holy D@t*! How to Deal with Imperfect, Unclean Datasets



- https://docs.google.com/presentation/d/1G-lgHKTdrqeeJhcvVmd7C9gOIfTRe429zhBN6lmKKzA/



Nora Neumann, Usable A/B testing – A Bayesian approach



- https://speakerdeck.com/nneu/b-testing-a-bayesian-approach



Frank Kaufer, Building Polyglot Data Science Platform on Big Data Systems



- https://speakerdeck.com/fkaufer/polyglot-data-science-platforms-on-big-data-systems



Lukasz Czarnecki, Brand recognition in real-life photos using deep learning



- http://de.slideshare.net/ukaszCzarnecki/brand-recognition-in-reallife-photos-using-deep-learning-lukasz-czarnecki-pydata-berlin-2016/



Edouard Fouché, Accelerating Python Analytics by In-Database Processing



- https://ibmdbanalytics.github.io/pydata-berlin-2016-ibmdbpy.slides.html



Delia Rusu, Estimating stock price correlations using Wikipedia



- https://speakerdeck.com/deliarusu/estimating-stock-price-correlations-using-wikipedia

- https://github.com/deliarusu/wikipedia-correlation



Jakob van Santen, The IceCube data pipeline from the South Pole to publication



- http://icecube.wisc.edu/~jvansanten/pasties/slides/2016-05-21%20PyData.pdf



Moritz Neeb, Bayesian Optimization and it's application to Neural Networks"



- https://slack-files.com/T18U1ASNQ-F1AHX36HG-22a535f1a2



Kashif Rasul, What's new in Deep Learning?



- https://bitly.com/new-deep-learning

- https://bitly.com/cifar10-resnet



Nathan Epstein, Machine Learning at Scale



- https://github.com/NathanEpstein/pydata-berlin



Ronert Obst and Dat Tran, PySpark in Practice



- http://pydata2016.cfapps.io/#/



Jose Quesada, A full Machine learning pipeline in Scikit-learn vs in scala-Spark: pros and cons



- https://files3.mixmaxusercontent.com/Qb5xzixaAsFjdsNbn/f/TZIGovHLNm7Is9Z5P/?messageId=lC47ZAHVG9riuwJkc&rn=Iibh1mclh2RgUnbpRkI&re=ISZk5ibpxmclJWLulmLul2dyFGZA5WYtJXZodmI



Martina Pugliese, Spotting trends and tailoring recommendations: PySpark on Big Data in fashion



- https://github.com/martinapugliese/talks_presentations/tree/master/pydata_berlin_2016



Angelos Kapsimanis, The Simple Leads To The Spectacular (Cancelled)



Anton Dubrau, Using small data in the client instead of big data in the cloud



- did not respond, yet



Nils Magnus, Dealing with TBytes of Data in Realtime



- did not respond, yet



Abhishek Thakur, Classifying Search Queries without User Click Data



- did not respond, yet



Jessica Palmer, Python and TouchDesigner for Interactive Experiments



- did not respond, yet



Maciej Gryka, Removing Soft Shadows with Hard Data



- did not respond, yet



Andreas Lattner, Setting up predictive analytics services with Palladium



- did not respond, yet



Andrej Warkentin, Visualizing FragDenStaat.de



- did not respond, yet



James Powell, The kwarg problem



- did not respond, yet



Matthew Honnibal, Designing spaCy: A high-performance natural language processing (NLP) library written in Cython



- did not respond, yet



Valentine Gogichashvili, Data Integration in the World of Microservices



- did not respond, yet



Michelle Tran Chain, Loop & Group: How Celery Empowered our Data Scientists to Take Control of our Data Pipeline



- did not respond, yet



Guertel Idai, Artificial Body Representation in Robots, Expectation and Surprise



- did not respond, yet



Robert Meyer, pypet: A Python Toolkit for Simulations and Numerical Experiments



- did not respond, yet



Juha Suomalainen, Visualizing research data: Challenges of combining different datasources



- did not respond, yet



Danny Bickson, Python based predictive analytics with GraphLab Create



- did not respond, yet



Fang Xu, Connecting Keywords to Knowledge Base Using Search Keywords and Wikidata



- did not respond, yet



Dr. Markus Abel, Python Learns to Control Complex Systems



- did not respond, yet



Tutorials

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Frank Gerhardt, Using Spark - with PySpark



- https://gitlab.com/gerhardt.io/pyspark-workshop



Mike Müller, Single-source Python 2/3



- http://www.python-academy.com/download/pydatabln2016/Single_Source_Python_2_3.pdf



Katharine Jarmul, Data Wrangling with Python



- https://github.com/kjam/data-wrangling-pycon



Lev Konstantinovskiy, Practical Word2vec in Gensim



- https://github.com/RaRe-Technologies/movie-plots-by-genre



Shoaib Burq, Which city is the cultural capital of Europe? An introduction to Apache PySpark for GeoAnalytics



Lightning Talks

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Oliver Zeigermann



- https://djcordhose.github.io/big-data-visualization/2016_pydata_berlin_lightning.html#/



Piotr Migdał, Teaching machine learning



- https://speakerdeck.com/pmigdal/teaching-machine-learning

- http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html



Mentioned tools:



- Pybuilder: Tired of writing setup.py? http://pybuilder.github.io/

- Sputnik: Package manager for Data https://github.com/spacy-io/sputnik