IDEs/ Jupyter Session List
10 ways to debug Python code
Christoph DeilCode-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 NeubauerDevOps, 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 TuloupCommunity, Data Science, IDEs/ Jupyter, Visualisation
A tour of 20 JupyterLab extensions, in 20 minutes. Demos included!
AI Intentions and Code Completion
Vasily KorfArtificial 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 EdesComputer 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 HeinzeComputer 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 BoschArtificial 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 SchmuddeData 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
Vincent WarmerdamArtificial 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 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!
Panel: Turn any notebook into a deployable dashboard
Philipp RudigerData Science, IDEs/ Jupyter, Visualisation
Introducing Panel: Turn any notebook into a deployable dashboard
Visualizing Interactive Graph Networks in Python
Jan-Benedikt JaguschData Science, IDEs/ Jupyter, Networks, Visualisation, Python
Join @jan_jagusch's talk to learn about visualizing interactive network graphs in Python.