Jupyter Digest: 17 April 2017

Reproducibility, TensorFlow examples, the new NBA, and 30,699 Kobe Bryant shots.

By Andrew Odewahn
April 17, 2017
Jupyter Digest. Jupyter Digest.
  • Number 8.5 will surprise you! Reproducibility is a hallmark of a solid analysis. Jake VanderPlas’ (@jakevdp) article “Reproducible Data Analysis in Jupyter” provides a detailed recipe you can use to improve this important but often neglected dimension of your work!
  • Practical TensorFlow. TensorFlow is one of the hottest new technologies out there, but can be a bit impenetrable. This notebook of TensorFlow examples is a great place to start if you want to kick the tires on this fast moving tool.
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  • Jupyter’s fast break. Mark Cuban’s (@mcuban) Tweet that Jupyter and machine learning are the new NBA shows how Jupyter is moving beyond the sciences and into some surprising new industries.
  • Speaking of Basketball. The LA Times Data Desk shows how to use Jupyter to see every shot by Kobe Bryant.
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