If you’re stuck at home due to the coronavirus pandemic, you can dedicate your free time to deepening your knowledge on machine learning. If you’ve already read all the books and available resources, check out this weekly roundup.
Here goes a dose of the latest news, discoveries, and inspiring stories. There is something for everyone. Enjoy your read!
Weekly Roundup: March 24th – 30th
> Neptune.ai blog – make sure to visit our blog to find out interesting and in-depth articles on machine learning.
Also, we’ve recently launched a podcast so make sure to listen to it. 🎧
> Best Python Libraries for Machine Learning and Deep Learning by Claire D. from Digitalogy | March 25
A nice comparison of the best Python libraries for modern machine learning models and projects.
> Machine Learning Tips that will save you hours of head-scratching by Roman Orac | March 25
Using these tips will drastically reduce redundant work. There will be less “What did I screw again” moments.
> Neural networks facilitate optimization in the search for new materials by David L. Chandler, MIT News Office | March 26
A fascinating read on how sorting through millions of possibilities, a search for battery materials delivered results in five weeks instead of 50 years.
Bernard Marr | March 24
Demand for people with artificial intelligence and machine learning skills has never been bigger. Demand clearly outstrips supply. If you want to boost your AI and ML skills, then these 10 free courses are a great place to start.
> Machine Learning Finds Just How Contagious (R-Naught) the Coronavirus Is by Andre Ye on Towards Data Science blog | March 23
And the author claims that “The answer is probably not what you think” but I won’t reveal more, make sure to check it out!
> 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
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 ->