Data science and machine learning in Jupyter Notebooks can lead to complicated code, making it hard to improve your projects. In this article, you will learn how to reduce complexity in your code, why it’s important to get your code out of Jupyter Notebooks as soon as possible, and how to keep your code clean.
For certain tasks, Jupyter users tend to switch to general-purpose IDEs. Therefore, the Jupyter project has decided to add a new feature that Jupyter users have been missing—a visual debugger in JupyterLab. Let’s take a closer look at the features of the debugging extension’s initial release.
HiPlot, a visualization tool for plotting high-dimensional data as is used in machine learning tasks, was released by Facebook AI. The open source tool supports parallel plots, can be run from a Jupyter Notebook and provides interactive visualizations. Let’s take a closer look at the features.