Creating an Interactive ML Conference Showcase
Harald Bosch
Artificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine Learning

Build a ML showcase using #transferlearning, #keras, #WebRTC, #python

Current affairs, updates, and the roadmap of scikit-learn and scikit-learn-contrib
Adrin Jalali
Artificial Intelligence, Community, Code-Review, Machine Learning

an update on recent scikit-learn changes, current affairs, and the roadmap

Developers vs. Enterprise
Ingo Stegmaier
Community, Use Cases

Developers vs. Enterprise. A guide to promote and succeed internal projects

Embrace uncertainty! Why to go beyond point estimators for valuable ML applications
Stefan Maier
Algorithms, Data Science, Machine Learning, Statistics

Usually, uncertainties of Machine Learning predictions are just regarded as a sign of poor prediction accuracy or as a consequence of lacking input features. This talk illustrates how modeling uncertainties can improve ML based decisions.

Julia for Python
Simon Danisch
Data Science, Infrastructure, IDEs/ Jupyter, Parallel Programming

Julia is a new Language, that is fast, high level, dynamic and optimized for Data Science. Learn about Julia's strengths and how you can integrate it in your Python workflow!

Leveraging the advantages of Bayesian Methods to build a data science product using PyMC3
Korbinian Kuusisto
Algorithms, Business & Start-Ups, Data Science, Machine Learning, Science, Statistics

How can one leverage the power of Bayesian methods to build a successful data science product?

Panel: Turn any notebook into a deployable dashboard
Philipp Rudiger
Data Science, IDEs/ Jupyter, Visualisation

Introducing Panel: Turn any notebook into a deployable dashboard

Should I stay or should I go? Optimal exercise decisions using the Longstaff-Schwartz algorithm
Benedikt Rudolph
Algorithms, Business & Start-Ups, Data Science, Science, Statistics

Learn about a simple least-squares approach to evaluate financial exercise options and make optimal exercise decisions.

Time series modelling with probabilistic programming
Sean Matthews, Jannes Quer
Data Science, Statistics

Probabilistic time-series forecasting @ Deloitte Analytics Institute

Why you should (not) train your own BERT model for different languages or domains
Marianne Stecklina
Artificial Intelligence, Deep Learning, Data Science, Natural Language Processing, Machine Learning, Science

Language models like BERT can capture general language knowledge and transfer it to new data and tasks. However, applying a pre-trained BERT to non-English text has limitations. Is training from scratch a good (and feasible) way to overcome them?