Conferences / PyData
PyData Eindhoven 2019
Nov 29–30, 2019 · Greenhouse, Emmasingel 29, Eindhoven (tutorials, 29 Nov); ASML Building 7, Veldhoven (talks, 30 Nov), Eindhoven, Netherlands
- Conference
- PyData Data science & ML
- Dates
- Nov 29, 2019 – Nov 30, 2019
- Venue
- Greenhouse, Emmasingel 29, Eindhoven (tutorials, 29 Nov); ASML Building 7, Veldhoven (talks, 30 Nov)
- Place
- Eindhoven, Netherlands
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- pydata.org
- Recorded
- 8 sponsors · 33 speaker appearances
- Notes
- Tutorials on 29 Nov at Greenhouse (Eindhoven); main conference on 30 Nov at ASML in Veldhoven. 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 8
Speakers and talks 33
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Marc Ponsen | — | Keynote | Keynote: Data Science at PSV | — |
| Maurits Kaptein | — | Keynote | Keynote: Personalization, Bandits, and Causal inference | — |
| Ruud van Elk | — | Keynote | Keynote: Data Science at PSV | — |
| Alexander Backus | — | Talk | Categorizing financial transactions for personal finance management at Yolt | — |
| Anca Dumitrache | FD Mediagroep | Talk | A content-based recommender system for financial news | — |
| Axel Goblet | BigData Republic | Tutorial | Scheduling machine learning pipelines using Apache Airflow | — |
| Colin van Lieshout | Soda | Talk | Automated river plastic monitoring using deep learning | — |
| Dimitra Gkorou | — | Talk | Deep Dive in Imbalanced Learning: a Framework in Python | — |
| Dimitra Gkorou | — | Tutorial | Deep generative models for image and text generation, | — |
| Ekhtiar Syed | ASML | Talk | Exploratory Data Analysis (EDA) and Visualization Techniques for Image Segmentation Challenges | — |
| Fahimeh Alizadeh | Randstad Digital | Talk | The 3 energy saving tips for your data science ecosystem | — |
| Feng Lu | FD Mediagroep | Talk | A content-based recommender system for financial news | — |
| Frank Hendriks | — | Talk | Solar energy disaggregation from smart meter signals: an approach using a convnet/resnet hybrid | — |
| Gerben Oostra | — | Talk | Preventing churn like a bandit | — |
| Jan van der Vegt | KLM Royal Dutch Airlines | Talk | Common pitfalls leading to wrongly estimated model performance | — |
| Jasper Derikx | Vantage | Talk | Next-Gen railway asset management with computer vision at ProRail | — |
| Jeroen Rombouts | — | Talk | Solar energy disaggregation from smart meter signals: an approach using a convnet/resnet hybrid | — |
| Joery de Vos | Soda Science | Talk | Automated river plastic monitoring using deep learning | — |
| Koen Vannisselroij | — | Tutorial | Deep generative models for image and text generation | — |
| Mattia Fornasa | Pacmed | Talk | Transferring clinical prediction models across hospitals using domain-adaptation techniques | — |
| Maurice Peters | — | Tutorial | Image Recognition DL WORKSHOP | — |
| Milan Mulji | Signify Technology | Tutorial | Managing Machine Learning Lifecycle with MLflow | — |
| Natasja van de L'Isle | TNO-GDN | Talk | BigMedilytics: MPyC in practice | — |
| Pipple | Pipple | Tutorial | Image Recognition DL WORKSHOP | — |
| Richard Bartels | Vantage | Talk | Next-Gen railway asset management with computer vision at ProRail | — |
| Rik Heijdens | JW Player | Talk | Airflow and Kubernetes at JW Player, a match made in heaven? | — |
| Ruben Mak | Greenhouse | Talk | Advertising, algorithms and Privacy: recent opensource developments | — |
| Satej Khedekar | ASML | Talk | A Python application to flag outliers in very high dimensional sensor data. | — |
| Sebastiaan J. van Zelst | — | Talk | Process Mining in Python | — |
| Shama Khalil | — | Tutorial | Deep generative models for image and text generation | — |
| Sonali Fotedar | — | Tutorial | Deep generative models for image and text generation | — |
| Vincent D. Warmerdam | — | Talk | Untitled12.ipynb | — |
| Vladimir Osin | Signify Technology | Tutorial | Managing Machine Learning Lifecycle with MLflow | — |