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Conferences / PyData

PyData Amsterdam 2019

May 10–12, 2019 · Booking.com (talks, 11-12 May); GoDataDriven (tutorials, 10 May), Amsterdam, Netherlands

Conference
PyData Data science & ML
Dates
May 10, 2019 – May 12, 2019
Venue
Booking.com (talks, 11-12 May); GoDataDriven (tutorials, 10 May)
Place
Amsterdam, Netherlands
Format
In person
Status
Past
Attendance
—
Website
pydata.org
Recorded
9 sponsors · 45 speaker appearances
Notes
Tutorials hosted by GoDataDriven on 10 May; conference talks hosted by Booking.com on 11-12 May. Videos on the PyData YouTube channel (playlist PLGVZCDnMOq0q7_6SdrC2wRtdkojGBTAht). Ticket prices not found. Speakers from the conference site's speaker list; affiliations parsed from speaker profile bios (null where not stated).

Ticket prices

No ticket prices recorded

Sponsors 9

Platinum 1

Silver 1

Other 2

Speakers and talks 45

SpeakerCompanyRoleTalkLinks
Sasha Romijn—KeynoteKeynote: Why our products and communities need our empathy—
Wouter KuijpersEindhoven University of TechnologyKeynoteKeynote: Robots for Football—
Ahmet Erdem—TalkGenetic RegexVideo
Alex ConwayNumberBoostTalkSebenz.ai - South African job creation through gamified data labeling for machine learning—
Alexander CS Hendorf—TalkSpeech Synthesis with Tacotron2 and PyTorchVideo
Amit BekaBooking.comTalkAnnotating data the right wayVideo
Anahita Farokhi—TalkText analytics on annual reportsVideo
Anni Sapountzi—TalkRemember what you learn: “A spaced repetition model for online skill acquisition"—
Camille CouturierBooking.comTutorialHands on: build a recommender systemVideo
Diederik GrevelingGoDataDrivenTalkBuilding a Multi-Core apply function for pandasVideo
Eltjona Qato—TalkText analytics on annual reportsVideo
Eva van Weel—TalkWhat’s the uncertainty on your ML prediction?Video
Fabian JansenINGTalkWhat’s the uncertainty on your ML prediction?Video
Floriana Zefi—TalkUsing Markov Chain approximation to identify financial fraudVideo
Francesco Cardinale—TalkLow to High Resolution: a walk through an Image Super-Resolution project with CNNs and GANsVideo
Hidde Hovenkamp—TalkSHAP and beyond: interpretable machine learning applications for intensive care doctorsVideo
Ivo Everts—TalkContinuous online experimentation: 101 A/B tests for personalised news.—
James Powell—TutorialPython Disfluencies—
Jan van der VegtKLM Royal Dutch AirlinesTalkA walk through the isolation forestVideo
Jonatan Samoocha—TalkHacking Fitness - Dealing With Uncertainty in Athletic Performance PredictionVideo
Jurjen FeitsmaIKETalkLooking at Sound: computer vision techniques in audio classificationVideo
Karlijn ZaanenBooking.comTalkEfficient Transfer Learning for Machine Translations @Booking.comVideo
Lena ShakurovaTextkernelTalkHow to expand your NLP solution to new languages using transfer learningVideo
Lucas Bernardi—TalkCasual notes about Causal stuffVideo
Maarten SukelChief Technology Office of the City of AmsterdamTalkOpen-source City - Improving livability of the city using publicly available resources.Video
Marcel RaasCERNTalkHaving fun with music and KerasVideo
Marianne HoogeveenBowery FarmingTalkPlant factory: sensor data, machine learning and optimization for data-driven vertical farmingVideo
Matthijs—TalkContinuous online experimentation: 101 A/B tests for personalised news.—
Max HalfordIRIT (Institut de Recherche en Informatique de Toulouse)TalkOnline machine learning with cremeVideo
Nick JonesUberTalkUber's Synthetic Control Experimentation Framework When A/B Tests are Not PossibleVideo
Ondrej Kokes—TalkHigh on cardinality, low on memoryVideo
Ralf Gommers—TalkThe evolution of array computing in PythonVideo
Ritchie VinkXomniaTalkBuild Facebook's Prophet in PyMC3; Bayesian time series analyis with Generalized Additive ModelsVideo
Robin Bakker—TalkGenetic RegexVideo
Sam BarrowsUberTalkUber's Synthetic Control Experimentation Framework When A/B Tests are Not PossibleVideo
Sanne VrijenhoekUniversity of AmsterdamTalkPopping the filter bubble: measuring diversity in news recommendationsVideo
Sarah Diot-Girard—TutorialHow to easily set up and version your Machine Learning pipelines, using DVC and MLV-tools—
Satendra KumarBooking.comTalkEfficient Transfer Learning for Machine Translations @Booking.comVideo
Stéphanie Bracaloni—TutorialHow to easily set up and version your Machine Learning pipelines, using DVC and MLV-tools—
Susanne GroothuisKPMGTalkCase Study: Making the subjective measurable. Sentiment Analysis on Sustainability reporting—
Swaan Dekkers—TalkOpen-source City - Improving livability of the city using publicly available resources.Video
Valentin Haenel—TutorialCreate CUDA kernels from Python using Numba and CuPy.—
Vincent D. WarmerdamGoDataDrivenTalkThe Profession of Solving (the Wrong Problem)Video
Wilder Rodriguesaigent.comTalkImproving Machine Learning Workflow - Training, Packaging and Serving your ModelsVideo
Wouter de Winter—TalkLooking at Sound: computer vision techniques in audio classificationVideo

Sources