pydata-berlin-2018 icon indicating copy to clipboard operation
pydata-berlin-2018 copied to clipboard

Published 20 hours ago • verified publisher icon lopusz
→

Metadata

  • Links to slides & materials from PyData Berlin 2018 ** Friday, 05.07.2018 *** Track 1
    • 09:00-09:30 [[https://www.slideshare.net/PyData/using-gans-to-improve-generalization-in-a-semisupervised-setting-trying-it-in-open-datasets-andreas-merentitis-carmine-paolino-vaibhav-singh][Using GANs to improve generalization in a semi-supervised setting - trying it in open datasets, Andreas Merentitis, Carmine Paolino, Vaibhav Singh]]
    • 10:45-12:15 Deep Neural Networks with PyTorch, Stefan Otte
      • https://github.com/sotte/pytorch_tutorial
    • 13:15-14:45 [[https://speakerdeck.com/mouradmourafiq/reproducing-and-scaling-machine-learning-experiments-with-polyaxon][Scaling and reproducing deep learning on Kubernetes with Polyaxon, Mourad Mourafiq]]
    • 15:00-16:30 [[http://plainpixels.work/resources/production-ready-datascience.slides#/][Production ready Data-Science with Python and Luigi, Mark Keinhörster]]
      • https://github.com/crazzle/pydata_berlin_2018
    • 16:45-18:15 [[https://bweigel.github.io/pydata_bln_2018/#/][Deploying a machine learning model to the cloud using AWS Lambda, Benjamin Weigel]] *** Track 2
    • 09:00-10:30 Tricks, tips and topics in Text, Analysis Bhargav Srinivasa Desikan
      • https://github.com/bhargavvader/personal/tree/master/notebooks
    • 10:45-12:15 Leveling up your storytelling and visualization skills, Gerrit Gruben
      • https://github.com/uberwach/leveling-up-viz-story
    • 13:15-14:45 A Hands-On Introduction to Your First Data Science Project Em Grasmeder, Jin Yang
      • https://github.com/ThoughtWorksInc/twde-datalab
    • 15:00-16:30 Search Relevance: A/B testing to Reinforcement Learning, Arnab Dutta
      • https://github.com/kraktos/MAB
    • 16:45-18:15 [[https://www.slideshare.net/PyData/deprecating-the-state-machine-building-conversational-ai-with-the-rasa-stack-justina-petraityt][Deprecating the state machine: building conversational AI with the Rasa stack Justina Petraitytė]] ** Saturday, 06.07.2018 *** Neural Networks
    • 11:00-11:45 [[https://www.slideshare.net/VaibhavSingh2/visual-concept-learning][Visual concept learning from few images, Vaibhav Singh]]
    • 11:45-12:30 [[https://www.dropbox.com/s/a7xako61ihuh82k/20180607_network_viz_pydata_berlin.pdf?dl=0][Simple diagrams of convoluted neural networks, Piotr Migdał ]] *** NLP & Text Analysis *** Python Applications
    • 11:00-11:45 [[https://speakerdeck.com/ellenkoenig/pydata-bln-2018-five-things-i-learned-while-prototyping-ml-papers][Five things I learned from turning research papers into industry prototypes, Ellen König]]
    • 11:45-12:30 [[https://github.com/dillongardner/PyDataSpatialAnalysis/raw/master/GeospatialAnalysis.pdf][Spatial Data Analysis With Python, Dillon R. Gardner]]
      • https://github.com/dillongardner/PyDataSpatialAnalysis
    • 12:30-13:15 [[https://github.com/SmokinCaterpillar/Snowballs/raw/master/pydata_berlin_2018_slides.pdf][Python Unittesting for Ethereum Smart Contracts or how not to create your own Cryptocurrency, Robert Meyer]]
      • https://github.com/SmokinCaterpillar/Snowballs *** Tools of the Trade
    • 11:45-12:30 [[https://github.com/ThorbenJensen/pydata2018berlin-hyperparameter-optimization/blob/master/pydata_hyperparameter.pdf][Towards automating machine learning: benchmarking tools for hyperparameter tuning, Thorben Jensen]]
    • 12:30-13:15 [[https://docs.google.com/presentation/d/e/2PACX-1vR0K9gtlPRGRIL6isoVWqa7SOr486yn9p_yCfH-ljtgQa2KpN0J03fOJa_jYgjeVwY3uAJe6GgAxez6/pub?start=false&loop=false&delayms=3000#slide=id.gc6f9e470d_0_0][Launch Jupyter to the Cloud: an example of using Docker and Terraform, Cheuk Ting Ho]]

*** Algorithms + 15:45-16:30 [[https://www.slideshare.net/PyData/solving-very-simple-substitution-ciphers-algorithmically-stephen-enrightward][Solving very simple substitution ciphers algorithmically, Stephen Enright-Ward]] *** NLP & RNNs + 14:15-15:00 [[https://speakerdeck.com/honnibal/building-new-nlp-solutions-with-spacy-and-prodigy][Building new NLP solutions with spaCy and Prodigy, Matthew Honnibal]] + 15:00-15:45 [[https://drive.google.com/file/d/1NkcdGRiTMXeNhqzOFRz6mjNEjnDN_F73/view][How I Made My Computer Write it's First Short Story, Alexander Hendorf]] + 15:45-16:30 [[https://www.dropbox.com/s/hri8veio4rep5g4/Self-Attention_for_NLP_by_Ivan_Bilan.pptx][Understanding and Applying Self-Attention for NLP, Ivan Bilan]] *** Python in the Field + 14:15-15:00 [[https://www.dropbox.com/s/wwfkbfd28rm8hoi/Pydata_Berlin_2018.pptx?dl=0][Python in Medicine: analysing data from mechanical ventilators and patient monitors Gusztav Belteki]] + 15:00-15:45 [[https://github.com/awakenting/master-thesis/blob/master/pydata_2018_presentation_slides.pdf][How to scare a fish (school), Andrej Warkentin]] *** Unsupervised Learning & Visualization + 14:15-15:00 [[https://de.slideshare.net/StefanKhn4/talk-at-pydata-berlin-about-manifold-learning-and-applications][Manifold Learning and Dimensionality Reduction for Data Visualization and Feature Engineering, Stefan Kühn]] + https://github.com/cc-skuehn/Manifold_Learning + 15:00-15:45 [[https://github.com/metterlein/spectral_clustering/blob/master/slides/SpectralClustering.pdf][Extracting relevant Metrics with Spectral Clustering, Evelyn Trautmann]] + https://github.com/metterlein/spectral_clustering/ + 15:45-16:30 [[https://juanitorduz.github.io/documents/orduz_pydata2018.pdf][On Laplacian Eigenmaps for Dimensionality Reductio, Juan Orduz]]

*** Lightning Talks + [[https://www.slideshare.net/lopusz/the-five-tribes-of-machine-learning-explainers][The Five Tribes of Machine Learning Explainers, Michał Łopuszyński]] ** Sunday, 07.08.2018 *** ML in Production + 10.15-11:00 [[https://axsauze.github.io/industrial-machine-learning/#/][Industrial ML - Overview of the technologies available to build scalable machine learning, Alejandro Saucedo]] + https://github.com/axsauze/industrial-machine-learning + https://github.com/axsauze/crypto-ml *** Explainability and Privacy + 10:15-11:00 [[https://www.slideshare.net/PyData/gdpr-in-practise-developing-models-with-transparency-and-privacy-in-mind-ukasz-mokrzycki][GDPR in practise - Developing models with transparency and privacy in mind, Łukasz Mokrzycki]] + 11:00:11:45 [[https://www.slideshare.net/figago/privacypreserving-data-sharing-pydata-berlin-2018][Privacy-preserving Data Sharing, Omar Ali Fdal]] + 11:45-12:30 [[https://github.com/dswah/PyData-Berlin-2018-pyGAM/blob/master/PyData_pyGAM_slides.pdf][pyGAM: balancing interpretability and predictive power using Generalized Additive Models in Python Dani Servén Marín]] *** ML in Production + 11:00-11:45 [[https://de.slideshare.net/FlorianWilhelm2/how-mobilede-brings-data-science-to-production-for-a-personalized-web-experience][How mobile.de brings Data Science to Production for a Personalized Web Experience, Florian Wilhelm and Markus Schüler]] *** Computer Vision & CNNs + 10:15-11:00 [[https://drive.google.com/file/d/1SDH8JSK4GW45RdDhUhR2b_mc-AxqeWuL/view][When to go deep in Computer Vision... and how, Irina Vidal Migallón]] + 11:45-12:30 [[https://www.slideshare.net/PyData/the-face-of-nanomaterials-insightful-classification-using-deep-learning-angelo-ziletti][The Face of Nanomaterials: Insightful Classification Using Deep Learning Angelo Ziletti]]

*** Best Practices + 14:15-15:00 [[https://www.slideshare.net/DmitryPetrov15/pydata-berlin-2018-dvcorg][Data versioning in machine learning projects, Dmitry Petrov]] *** Extending Python + 13:30-14:15 [[https://datawookie.github.io/talk-mixed-python-r/][Interfacing R and Python, Andrew Collier]] + 14:15-15:00 [[https://www.slideshare.net/xhochy/extending-pandas-using-apache-arrow-and-numba][Extending Pandas using Apache Arrow and Numba, Uwe L. Korn]] *** Bayesian Methods + 13:30-14:15 [[https://github.com/junpenglao/All-that-likelihood-with-PyMC3/blob/master/All%20that%20likelihood.pdf][All that likelihood with PyMC3, Junpeng Lao]] + https://github.com/junpenglao/All-that-likelihood-with-PyMC3

*** Performance + 15:15-16:00 [[https://bigdata.uni-saarland.de/publications/Big%20Data%20Systems%20Performance%20-%20The%20Little%20Shop%20of%20Horrors.pdf][Big Data Systems Performance: The Little Shop of Horrors, Jens Dittrich]] + 16:00-16:45 [[https://github.com/TwentyBN/20bn-video-data-loading-talk/blob/master/20bn-video-data-loading-talk-PyDataBerlin2018.01.pdf][Battle-hardened advice on efficient data loading for deep learning on videos, Valentin Haenel]] + https://github.com/TwentyBN/20bn-video-data-loading-talk *** New Libraries + 16:00-16:45 [[https://www.slideshare.net/PyData/lightfieldsjl-fast-3d-image-reconstruction-for-vr-applications-hector-andrade-loarca][LightFields.jl: Fast 3D image reconstruction for VR applications Hector, Andrade Loarca]] *** Visualization Tools + 15:15-16:00 [[https://janpipek.github.io/pydata2018-berlin/slides/#/][Meaningful histogramming with Physt, Jan Pipek]] + https://janpipek.github.io/pydata2018-berlin/ + 16:00-16:45 [[https://github.com/jtpio/pixijs-jupyter/blob/master/examples/presentation.ipynb][Practical examples of interactive visualizations in JupyterLab with Pixi.js and Jupyter Widgets, Jeremy Tuloup]] + https://github.com/jtpio/pixijs-jupyter + Binder version: https://mybinder.org/v2/gh/jtpio/pixijs-jupyter/pydata-berlin?urlpath=lab/tree/examples/presentation.ipynb

*** Lightning Talks + Missing talk ;) [[https://github.com/sotte/pydata_eda_lightning_talk/blob/master/demo.ipynb][Some tools to ease EDA, Stefan Otte]] ** Notes, write-ups

  • Notes by [[https://twitter.com/liopic][@liopic]]
    • https://gist.github.com/liopic/6f1a6d50d41bd07efc18c876329ab254
  • Notes by jznf
    • https://gitlab.com/jznf/pydata-berlin-2018

← Metadata

15
Stars
3
Forks
Watchers

Owner

verified publisher icon lopusz

Metadata

Back