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This Week in Machine Learning: ML & Remote Work, Useful Tools, and Neural Networks

Every day interesting things happen in the world of Data Science. And if you’re staying at home due to the coronavirus pandemic, make sure to check the best picks from machine learning in breaks between work.

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

Weekly Roundup: March 9th – 15th

> Machine Learning and Remote Work by Eero Laaksonen || March 13

A helpful insight for those who had to go remote. Short and to the point.

> Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning by Alan Ho, Product Lead and Masoud Mohseni, Technical Lead, Google Research on Google AI Blog || March 9

Together in collaboration with the University of Waterloo, X, and Volkswagen, Google announced the release of TensorFlow Quantum (TFQ), an open-source library for the rapid prototyping of quantum ML models.

> Artificial intelligence and machine learning spearhead a silent revolution in the field of law by Karan Kalia || March 12

The artificial intelligence revolution has the capability to transform the legal sector in various ways, and the industry is now catching up with the trend. Check out how machine learning is changing the field of law.

> The Most Useful Machine Learning Tools of 2020 by Ian Xiao on KDNuggets

The list of ML tools to use in 2020.

> Neural Networks are Surprisingly Modular – a research paper by Daniel Filan, Shlomi Hod, Cody Wild, Andrew Critch, Stuart Russell || March 10

The authors introduce a measurable notion of modularity for multi-layer perceptrons (MLPs), and investigate the modular structure of MLPs trained on datasets of small images. This and more you can find in this research paper.

> Machine Learning Takes On Antibiotic Resistance by Katherine Harmon Courage || March 9

An interesting read on how a deep learning neural network has helped to discover a novel antibiotic with an unconventional mechanism of action.

And a research paper on the subject: A Deep Learning Approach to Antibiotic Discovery

> Everything you need to become a self-taught Machine Learning Engineer by Jason Benn on Medium || March 13

A quick, helpful guide from a Machine Learning Engineer at a well-funded ML startup in Silicon Valley on how to become a self-made machine learning engineer.

> 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. 

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