One of our goals is to bring Jupyter’s enterprise use cases and practices into one place.
Both reproducible science and open source are necessary for collaboration at scale—the nexus for that intermingling is Jupyter.
Discover how data-driven organizations are using Jupyter to analyze data, share insights, and foster practices for dynamic, reproducible data science.
Attend a day-long exploration of Jupyter's best practices and practical use cases in business and industry.
Nadia Eghbal explores how money can support open source development without changing its incentives.
Lorena Barba explores how we can build a capacity to support reproducible research into the design of tools like Jupyter.
Jeremy Freeman describes a growing ecosystem of scientific solutions, many of which involve Jupyter.
William Merchan shares fundamental trends driving the adoption of Jupyter and its deployment in large organizations.
Andrew Odewahn explains how O’Reilly Media applied the Jupyter architecture to create the next generation of technical content.
Brett Cannon looks at how healthy expectations can maintain a balanced relationship between open source users and project maintainers.
Labz ‘N Da Wild 2.0: Teaching signal and data processing at scale using Jupyter notebooks in the cloud
Demba Ba explains how he designed and implemented two Harvard courses that use cloud-based Jupyter notebooks.
Peter Wang talks about the co-evolution of Jupyter and Anaconda and looks at what’s needed to sustain an open and innovative future.
Rachel Thomas shares her experience using Jupyter notebooks to help students understand deep learning through experimentation.
Wes McKinney makes the case for a shared infrastructure for data science.
Fernando Perez explains how Project Jupyter fits into a vision of collaborative development of tools that are applicable to research, education, and industry.
Watch highlights covering Jupyter notebooks, data management, collaborative data science, and more. From JupyterCon in New York 2017.
Jupyter in education, Jupyter-in-the-loop, and reproducibility in science.
A step-by-step tutorial on how to install and run JupyterHub on gcloud.
Learn how to use PixieDust in Jupyter Notebooks to create quick, easy, and powerful visualizations for exploring your data.
It’s pretty easy to grasp the concept, but it’s a tricky algorithm to implement.
An algorithm that generates Bézier curves using an increasing number of control points.
An algorithm for rubber-banding random points.
Jupyter for sharing and prototyping, Jupyter in academia, and FAIR principles.
To succeed in digital transformation, businesses need to adopt tools that enable collaboration, sharing, and rapid deployment. Jupyter fits that bill.
Giving context to code, human-in-the-loop design pattern, and collaborative documents.
Ring stacking games. With computers.
Approaches to data analysis, iterative workflows, and writing a book with Jupyter.
Getting started with data science, Jupyter as shareable hub, and JupyterLab adoption.
Jupyter as a learning tool, the JupyterHub Project, and Music21.
Script generation from RNNs, Tensorflow book companion notebooks, transportation insights from notebooks, machine learning notebooks.
Project Jupyter co-founder Brian Granger on the JupyterLab project, its potential role in scientific and tech communities, and the expanding role of notebooks.
TensorFlow cookbook materials, source notebooks, Python lectures, and Software Carpentry.
TSFRESH, 100 days of algorithms, how JupyterHub tamed big science, colorizing photos.
Opinionated Docker stacks, Jupyter Themes, Jupyter in the bank, and Zuckerberg's man in the lab.
JupyterDay Philly, Harmonics deep dive, Jupyter building blocks, and autoencoded Pokémon.
Python cheat sheet, open source DL guide, Keen IO, and digital signal processing.
This excerpt from Jake VanderPlas' Python Data Science Handbook
Reproducibility, TensorFlow examples, the new NBA, and 30,699 Kobe Bryant shots.