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This Week in Machine Learning: Onboarding AI, Top Leaders, ML & Dogs, and More

Data science is great. It brings a lot of good to the world, helps in developing new ways of aiding people in their work, daily lives, and improves how things function. Each day something new and fascinating happens. As every week, we’ve gathered for you the best articles on machine learning.

Check out the weekly roundup for the latest news from machine learning. News, analysis, interesting stories. Read, learn, and get inspired!

Weekly Roundup: June 16 – 22

» Neptune.ai blog – make sure to visit our blog to find out interesting and in-depth articles on machine learning from the last week.

» A Better Way to Onboard AI by Boris Babic , Daniel L. Chen , Theodoros Evgeniou and Anne-Laure Fayard on Business Harvard Review

“The greater the degree of organizational focus on people helping AI, and AI helping people, the greater the value achieved.” Read how to properly onboard AI (and people who are to work with it) within an organization.

» Top 10 Data Science Leaders You Should Follow in 2020 by Admond Lee on Towards Data Science | June 20

If you’re looking for inspirational people to learn from, make sure to check out the list.

Also, for more great minds, see our list – Top Machine Learning Influencers – All The Names You Need to Know

» Slightly Unnerving AI Produces Human Faces Out of Totally Pixelated Photos by David Nield on Science Alert | June 17

Artificial intelligence networks have learned a new trick: being able to create photo-realistic faces from just a few pixelated dots, adding in features such as eyelashes and wrinkles that can’t even be found in the original.

» Machine Learning Identifies Proteins Associated with Drug Side Effects on genengnews.com | June 18

Machine learning, when used properly, can be a life savior. A multi-institutional group of researchers led by Harvard Medical School and the Novartis Institutes for BioMedical Research (NIBR) report that it has created an open-source machine learning tool that identifies proteins associated with drug side effects.

» This startup could be a dog owner’s best friend as it uses machine learning to help guide key decisions by Kurt Schlosser on GeekWire | June 19

Ever wondered what your dog really needed? No more! Check out Scout9 that aids in dog’s development to meet their real needs.

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

» Old but gold, the reliable Reddit thread on ML for more news on machine learning. There’s always something for everyone – tips, tricks, hacks, and more news.


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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