Hello Neptuners and welcome to this (and previous) week’s roundup. As every 7 days or so, we’d like to share with you the best and most interesting articles we’ve found on the web this time. There’s already a lot to read so let’s get right to it.
Weekly Roundup: September 8-21
» 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.
A list of AI, ML, Deep Learning, Python, and Data Science Free Ebooks available on the internet! 📚
Is it possible that the entire universe on its most fundamental level is a neural network? In this article, you’ll find a short Q&A with Vitaly Vanchurin, a physics professor at the University of Minnesota Duluth, who talks about the universe as a neural network.
» AI ethics groups are repeating one of society’s classic mistakes by Abhishek Gupta Victoria Heath on MIT Technology Review | September 14
A great read on the importance of cultural and regional context’s importance for the ethical use of artificial intelligence. Developing a standardized ethos for AI requires acknowledging diversity. Read more in the article!
» How do we know AI is ready to be in the wild? Maybe a critic is needed by Tiernan Ray on ZDNet | September 18
In this article, the author poses a great question, “Given the risk, how can society know if a technology has been adequately refined to a level where it is safe to deploy?” And here’s where the critic comes into action–another algorithm within the same program that acts adversarially. You can read more about the approach known as conservative Q-Learning in the article.
» TOP 10 FASCINATING MOVIES ON DATA SCIENCE, MACHINE LEARNING & AI by Adilin Beatrice on Analytics Insight | September 18
We all need a little break from work, so why not watch a good movie? Here’s the list of some good movies on ML and AI. 🎥
» Can robots write? Machine learning produces dazzling results, but some assembly is still required by Alexandra Louise Uitdenbogerd on The Conversation | September 18
Check out the article for the text generated using the latest neural network model for language, called GPT-3, released by the American artificial intelligence research company OpenAI. (GPT stands for Generative Pre-trained Transformer.)
» 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.
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
Jakub Czakon | Posted November 26, 2020
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 ->