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

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!


READ NEXT

ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

10 mins read | Author Jakub Czakon | Updated July 14th, 2021

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

Continue reading ->
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