MLOps Blog

This Week in Machine Learning: Books, Quantum Computing & More

2 min
Pawel Kijko
14th November, 2022

Machine learning is such a vast field of science that it’s impossible to wrap your head around it all. New information is generated every millisecond, so how to comprehend it all? It’s a difficult task but to help you find out what’s happening in machine learning, I gathered the best resources.

Here goes a dose of the latest news, discoveries, and inspiring stories. There is something for everyone. Enjoy your read!

Weekly Roundup: March 16th – 23rd

Don’t learn machine learning: Learn how to build software with ML models by Caleb Kaiser | March 19

A great article for developers who want to build products with machine learning pointing. Note: it’s not for researchers! The author accurately noticed that most of the introductory material for machine learning is aimed at ML researchers and not developers. Read the article for more!

> How to Use Machine Learning Models to Predict Customer Turnover by James Ng on Hackernoon | March 17

The author explores 8 predictive analytic models to assess customers’ propensity or risk to churn. Read if you struggle with high customer turnover.

24 Best (and Free) Books To Understand Machine Learning by Reashikaa Verma on KDnuggets | March 20

A list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field.

> Machine learning to scale up the quantum computer by Dr Muhammad Usman and Professor Lloyd Hollenberg, University of Melbourne | March 17

An interesting read on how machine learning techniques could play a crucial role in this aspect of the realization of a full-scale fault-tolerant universal quantum computer—the ultimate goal of the global research effort.

–> Related article – Machine Learning Pushes Quantum Computing Forward | March 18

> Researchers Release Open Source Counterfactual Machine Learning Library | March 20

Researchers at Microsoft have released an open source code library for generating machine learning counterfactuals. The PureAI editors talked to Dr. Amit Sharma, one of the project leaders, and asked him to explain what machine learning counterfactuals are and why they’re important.

> KDnuggets™ News of the week with top stories and tweets of the past week, plus opinions, tutorials, events, webinars, meetings, and jobs

> Don’t forget about the reliable Reddit thread on ML for more news on machine learning!

That’s all folks! I hope you found something of interest in this weekly roundup. Don’t forget to check our blog for more inspiring articles.

Came across an interesting ML article? Or maybe you wrote one yourself and would like to share it with other people? Let us know, we’ll spread the news in our weekly roundup!