Machine Learning has application in so many different fields, that sometimes it may be hard to keep track of all the new things happening every day. From simple machines to physics, chemistry, biology, and the human brain – AI is used everywhere.
Check out what has happened over the past week in our weekly update!
Weekly Roundup: October 6-12
» Neptune.ai blog – as always, make sure to visit our blog to find out interesting and in-depth articles on machine learning from the last week. 🙂 We’ve published a lot of new content recently so don’t wait, go through our blog!
» Machine learning speeds up quantum chemistry calculations by Emily Velasco, California Institute of Technology on Phys.org | October 7
Quantum chemistry has its limits. But thanks to a new quantum chemistry tool OrbNet (developed through a partnership between Caltech’s Tom Miller, professor of chemistry, and Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences) that uses machine learning, quantum-chemistry calculations can be performed 1,000 times faster than previously possible, allowing accurate quantum chemistry research to be performed faster than ever before. ⚛️
» Going Beyond Human Brains: Deep Learning Takes On Synthetic Biology on SciTechDaily | October 7
Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around the lack the predictability of binary code in our bodies.
They developed a set of machine learning algorithms that can analyze reams of RNA-based “toehold” sequences and predict which ones will be most effective at sensing and responding to the desired target sequence. It has a large potential for saving human lives in the future! 🧬
» Someone let a GPT-3 bot loose on Reddit — it didn’t end well by Thomas Macaulay on The Next Web | October 7
So what can go wrong if you let a GPT-3 bot spend time around Reddit? Fortunately, nothing harmful 😉 But it’s still interesting (and funny) so go check yourself 🙂
» How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems by Jesus Rodriguez on KDnuggets | October 8
LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations. If you want to know more about it, just check out the article. A good, practical read!
» The Gap: Where Machine Learning Education Falls Short by Jupinder Parmar on The Gradient | October 10
A great article for all those who want to begin a career in AI. The author discusses whether the current state of Machine Learning Education is focusing on what really matters. More in the article, we encourage you to read it and form your own opinion! 🧑🎓
» Relationships and machine learning: What do they have in common? By Federico Urena on Towards Data Science | October 8
We won’t reveal much of this article. If you’re curious, make sure to read it 😉 💕
» 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! Have you found something of interest in this weekly roundup? We hope you got inspired! Don’t forget to check our blog for more inspiring articles.
👉 And if you came across an interesting ML article, or maybe wrote one yourself and would like to share it with other people, let us know, we’ll spread the news in our weekly roundup! See you next week!
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 ->