It’s been two weeks since we posted our weekly roundup. It’s time for an update! If you’re curious what’s been happening in the machine learning world over the past two weeks, keep reading, we’ve selected only the best content.
Check out our selection of the best articles, news, and stories. Read, learn, and get inspired!
Bi-Weekly Roundup: June 2-15
» Neptune.ai blog – make sure to visit our blog to find out interesting and in-depth articles on machine learning from the last week.
» New machine learning model predicts which base editor performs best to repair thousands of disease-causing mutations by Harvard University on phys.org | June 12
BE-Hive’s machine learning model predicts which base editor performs best to repair thousands of disease-causing mutations. In a word—an interesting read about how machine learning can be helpful in genetics studies 🧬
» Machine learning helps geoboffins spot huge beds of hot rocks 1,000km across deep below Earth’s surface by Katyanna Quach on The Register | June 15
Scientists are still learning how our planet has evolved. 🌎 Now, geophysicists have uncovered large swathes of hot, dense rock lying nearly 3,000 kilometres beneath Earth’s surface, hidden below the Pacific Ocean, thanks to an unsupervised learning algorithm.
> Here you can find the original paper: Sequencing seismograms: A panoptic view of scattering in the core-mantle boundary region
» Good Questions, Real Answers: How Does Facebook Use Machine Learning to Deliver Ads? on Facebook Business | June 11
A short read about how Facebook use machine learning to show you the relevant ads.
» 18 Handy Resources for Machine Learning Practitioners by Al Gharakhanian on Data Science Central | June 10
Take a look at this list to expand your library of useful resources.
» 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!
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 ->