Harald BoschArtificial Intelligence, Computer Vision, Deep Learning, IDEs/ Jupyter, Machine Learning
Build a ML showcase using #transferlearning, #keras, #WebRTC, #python
Adrin JalaliArtificial Intelligence, Community, Code-Review, Machine Learning
an update on recent scikit-learn changes, current affairs, and the roadmap
Ingo StegmaierCommunity, Use Cases
Developers vs. Enterprise. A guide to promote and succeed internal projects
Stefan MaierAlgorithms, 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.
Simon DanischData 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!
Korbinian KuusistoAlgorithms, 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?
Philipp RudigerData Science, IDEs/ Jupyter, Visualisation
Introducing Panel: Turn any notebook into a deployable dashboard
Benedikt RudolphAlgorithms, Business & Start-Ups, Data Science, Science, Statistics
Learn about a simple least-squares approach to evaluate financial exercise options and make optimal exercise decisions.
Sean Matthews, Jannes QuerData Science, Statistics
Probabilistic time-series forecasting @ Deloitte Analytics Institute
Marianne StecklinaArtificial 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?