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Data

Our take on the ideas, information, and tools that make data work.

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Jupyter at O’Reilly

By Andrew Odewahn | August 25, 2017

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The give and take of open source

By Brett Cannon | August 25, 2017

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Where money meets open source

By Nadia Eghbal | August 25, 2017

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Highlights from JupyterCon in New York 2017

By Mac Slocum | August 24, 2017

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Project Jupyter: From interactive Python to open science

By Fernando Pérez | August 24, 2017

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How the Jupyter Notebook helped fast.ai teach deep learning to 50,000 students

By Rachel Thomas | August 24, 2017

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Data science without borders

By Wes McKinney | August 24, 2017

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Labz ‘N Da Wild 2.0: Teaching signal and data processing at scale using Jupyter notebooks in the cloud

By Demba Ba | August 24, 2017

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The current state of applied data science

By Ben Lorica | August 24, 2017

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How Ray makes continuous learning accessible and easy to scale

By Ben Lorica | August 17, 2017
How Ray makes continuous learning accessible and easy to scale
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How to choose a cloud provider

By Jesse Anderson | August 11, 2017

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Why AI and machine learning researchers are beginning to embrace PyTorch

By Ben Lorica | August 3, 2017
Why AI and machine learning researchers are beginning to embrace PyTorch
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How big data and AI will reshape the automotive industry

By Ben Lorica | July 20, 2017
How big data and AI will reshape the automotive industry
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The SMACK stack

By Patrick McFadin | July 10, 2017

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A framework for building and evaluating data products

By Ben Lorica | July 6, 2017
A framework for building and evaluating data products
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Building a next-generation platform for deep learning

By Ben Lorica | June 29, 2017
Building a next-generation platform for deep learning
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