You can use Jupyter Notebooks to share Python code and its output with third parties, to run code with live interactive feedback, or to systematically track and document the progress of your work.

Jupyter Notebook use cases The most common use cases for Jupyter Notebook are data science, mathematics, and other research projects that involve visualizations of data or formulas.

In this video from the Blue Waters Symposium, Roland Haas from NCSA presents: Tutorial: How to use Jupyter Notebooks. "Jupyter notebooks provide a web-based interface to Python, R, Julia and other ...

Jupyter Notebooks on Google Cloud Hundreds of Jupyter Notebooks for the most popular AI use-cases. One click to run NGC Jupyter Notebooks on a Google Cloud Vertex AI Workbench.

Improved IntelliSense support for Jupyter Notebooks highlights the September 2022 update of Python dev tooling in Visual Studio Code. That tooling comes in extensions for Python (64 million installs, ...

Among the Jupyter extensions is the ability to “decompose” different AI development tasks into different notebooks, allowing each to use different frameworks such as TensorFlow for deep learning ...

Jupyter Notebooks, even though tightly tied to data science darling programming language Python, can now be done with .NET languages C# or F#. The popular notebooks provide interactive environments -- ...

Grausoft.net offers practical AI/ML tutorials and Jupyter Notebooks with production-ready code — built for learners, developers, and small businesses.

Deepnote, a startup that is building a data science platform on top of Jupyter-compatible notebooks, today announced that it has raised a $20 million Series A round co-led by Index Ventures and ...