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.

Any Python module that runs well in a notebook interface (essentially, anything that writes to stdout as part of its behavior) is a good candidate for this. Sharing code and data generally.

The advantage of using Jupyter instead of an ordinary interactive interpreter like Python's is that Jupyter automatically saves a record of what you do.

Installing Python This step may sound redundant if youâ re already knee-deep into programming, but youâ ll need to install Python on your PC to use GPU-accelerated AI in Jupyter Notebook.

It’s common for us to work on these projects using Jupyter notebooks because they’re great, fast, simple, and they allow us to interact and play with our data.

Because of the client/server architecture, it is very simple to share notebooks with other users. You can — in theory — use Jupyter for anything you could use Python for.

Use this in-depth Jupyter Notebook vs. PyCharm comparison to learn more about their features and pricing and to find your best fit.

Google Colab and Jupyter Notebook are powerful tools for coding and data analysis, each offering unique features and benefits. Compare them to choose the best fit for your needs.