Marysia Winkels, James HaywardTime Series
From inventory to website visitors, resource planning to financial data, time-series data is all around us. Knowing what comes next is key to success in this dynamically changing world. So join us and learn about time series analysis and seasonality modelling.
Tanmoy BandyopadhyayAlgorithms, Coding / Code-Review, Parallel Programming / Async
Use Python Inter Process Communication and Synchronization techniques effectively
Mike MüllerAlgorithms, Architecture, Python fundamentals
Functions that take functions and return new functions can be fun. Python's everything-is-an-object principle at work.
Richard PelgrimAPIs, Big Data, Cloud
A hands-on introduction to methods for scaling your data science and machine learning with Dask.
Philipp Rudiger, Maxime LiquetData Visualization, Jupyter, Science
Do you use the .plot() API of pandas or xarray? Do you ever wish it was easier to try out different combinations of the parameters in your data-processing pipeline? Follow this tutorial to learn how to easily build interactive plots and apps with hvPlot.
Torsten ZielkeBest Practice, Backend, Coding / Code-Review
Learn how to use testdriven development to boost your productivity and let the community do the annoying frequent checkups if the application still works
Guillaume LemaitrePredictive Modelling, Statistics, Transparency / Interpretability
Inspect and try to interpret your scikit-learn machine-learning models
Tobias SterbakBest Practice, Predictive Modelling, Reproducibility
Learn the basics of MLops with MLflow to manage the machine learning life-cycle.
DavidBest Practice, Development Methods, Reproducibility
In this workshop, we will learn what it means and how to build an "MLOps workflow" by extending the power of Git and GitHub with open-source tools.
Julia OstheimerBest Practice, Business & Start-Ups, Career & Freelancing, Corporate, Diversity & Inclusion, Ethics (Privacy, Fairness,… ), Transparency / Interpretability, Use Case
You wanna know how you can explain your grandparents what #MachineLearning is? Attend the #PyConDE #PyData tutorial on how to translate #ML terms into everyday language of any audience. #communication #101 #tutorial #softskills #AI
Valerio MaggioNeural Networks / Deep Learning, Security
Have you ever wondered how to train your @PyTorch model on private data you cannot see? If you want to know how, this is the workshop for you! #PPML cc/ @openminedorg
Aleksander MolakGraphs, Neural Networks / Deep Learning
Practical Graph Neural Networks (GNNs) with Spektral & TensorFlow 🤩
Florian BruhinBest Practice, Development Methods
The #pytest tool presents a rapid and simple way to write tests for your Python code. This training gives an introduction with exercises to some distinguishing features.
Dr. Kristian RotherBest Practice, Coding / Code-Review, Development Methods
Refactor a space travel game by introducing functions, classes and data structures
Prabhant SinghBest Practice, Community, Science
Learn how to create reproducible workflows, benchmarks and studies with openml-python
sktime - python toolbox for time series: advanced forecasting - probabilistic, global and hierarchical
Franz KiralyAlgorithms, Predictive Modelling, Time Series
The forecasting module of sktime provides a unified, sklearn-compatible, and composable interface. This tutorial covers advanced topics in forecasting using sktime: probabilistic forecasting, and forecasting with panel data, including global/hierarchical forecasting.
Jessica Greene (she/her), Vanessa AguilarData Visualization, DevOps, Performance
We know what your app did last summer. Do you? Join us for this practical & theoretical session if you’re looking to grasp the key concepts of observability, useful metrics, and ensuring operational excellence for your Python applications using Prometheus!