View profile weekly - Issue #30: investigating OpenAI, the risks of AutoML, Hyperopt on Spark, and more...


nibble dispatch

October 31 · Issue #30 · View online

Curated essays about the future of Data Science. Production Data Science and learning resources for continuous learning. Covers Data Science, Data Engineering, MLOps & DataOps. Curated by people at

Hi everyone,
Here are the cherry-picks for this edition: an investigation about OpenAI following the recent controversy around their announcement of the “Rubik’s cube solving”, the risks of AutoML and how to avoid them by the Harvard Business Review, a new release of Hyperopt with support for distributed tuning with Apache Spark, the launch of a data streaming nanodegree by Udacity and summaries of some very interesting research papers and more…

Would you spend $1 billion for a Rubik's cube?
Would you spend $1 billion for a Rubik's cube?
I spent $1 billion and all I got was a Rubik's cube
The Risks of AutoML and How to Avoid Them
Glue Work in Analytics
Military artificial intelligence can be easily and dangerously fooled
How notebooks are changing the way we develop code
Career / management
Your Data Analysts Need These 4 Qualities For Optimal Success
Hyperopt 0.2.1 includes distributed tuning via Apache Spark
Udacity launches Data Streaming Nanodegree
Stop explaining black-box machine learning models for high stakes decisions and use interpretable models instead
Exponentially Growing Learning Rate for Deep Learning?
Top three mistakes with K-Means Clustering during data analysis
End notes
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On a side note, we’re organizing events to connect data professionals in Paris. If you’re reading this, we probably have a lot to talk about and we’d love for you to join us. Next networking event is scheduled for wednesday 13th, november, it’s free but requires an invitation: if you’re interested or want to know more, shoot me an email at [email protected].
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