10 ways to debug Python code
Christoph Deil
Code-Review, IDEs/ Jupyter

Learn 10 ways to debug your Python code and many tips and tricks for effective debugging in 30 minutes.

6 Years of Docker: The Good, the Bad and Python Packaging
Sebastian Neubauer
DevOps, Infrastructure, IDEs/ Jupyter, Use Cases

In this talk I will walk you through the proper setup of a local python development environment using docker.

A Tour of JupyterLab Extensions
Jeremy Tuloup
Community, Data Science, IDEs/ Jupyter, Visualisation

A tour of 20 JupyterLab extensions, in 20 minutes. Demos included!

AI Intentions and Code Completion
Vasily Korf
Artificial Intelligence, Code-Review, IDEs/ Jupyter, Python

Datalore supports intentions – code suggestions based on what you’ve just written.

Birds of a feather flock together - Tracking pigeons with Python and OpenCV
Neslihan Edes
Computer Vision, IDEs/ Jupyter, Science

In this talk I want to demonstrate how to leverage existing Open Source technologies to implement basic movement tracking use cases.

Build a Machine Learning pipeline with Jupyter and Azure
Daniel Heinze
Computer Vision, Deep Learning, DevOps, IDEs/ Jupyter, Machine Learning, APIs, Python

Build a Machine Learning pipeline with Jupyter and Azure: https://notebooks.azure.com/Starlord/projects/pycon-ml-jupyter-azure

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

Dr. Schmood's Notebook of Python Calisthenics and Orthodontia
David Schmudde
Data Science, IDEs/ Jupyter

In "Mr. Schmudde's Notebook of Python Calisthenics and Orthodontia" @dschmudde explores the benefits of taking a functional approach in Jupyter notebooks. Don't get bit by misaligned state and output, keep your notebooks running with these functional tips! https://www.example.com

Gaussian Progress
Vincent Warmerdam
Artificial Intelligence, Algorithms, Data Science, IDEs/ Jupyter, Machine Learning, Statistics

gaussian progress. it's meta, but also the most normal conference title this year!

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!

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

Introducing Panel: Turn any notebook into a deployable dashboard

Visualizing Interactive Graph Networks in Python
Jan-Benedikt Jagusch
Data Science, IDEs/ Jupyter, Networks, Visualisation, Python

Join @jan_jagusch's talk to learn about visualizing interactive network graphs in Python.