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.

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.

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.

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 ...

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