Jupyter creates interactive documents to mix code, text, and graphics. Jupyter is popular for sharing code and results in scientific computing. Jupyter notebooks consist of Markdown and code cells ...

You can use existing models, convert Python TensorFlow models, use transfer learning to retrain existing models with your own data, and develop models from scratch.

TOOLBOX 30 October 2018 Why Jupyter is data scientists’ computational notebook of choice An improved architecture and enthusiastic user base are driving uptake of the open-source web tool.

Data visualizations. Most people have their first exposure to Jupyter Notebook by way of a data visualization, a shared notebook that includes a rendering of some data set as a graphic.

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.

Our data science doctor provides a hands-on neural networking tutorial to explain how to get started with the popular Keras library, a high-level wrapper over TensorFlow.

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.