Algorithms and numbers are all around us, we just don’t realize how much they affects our daily lives. In our weekly roundup, we present you the most interesting stories from the world of machine learning and data science.
If you’re interested in what has happened in the machine learning realm over the last week, see what we’ve gathered for you.
Weekly Roundup: September 1-7
» 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.
» This know-it-all AI learns by reading the entire web nonstop by Will Douglas Heaven on MIT Technology Review | September 4
One of the main advantages of ML over people is the ability to function at full capacity non-stop. Stanford startup Diffbot system is building an AI that reads every page on the entire public web, in multiple languages, and extracts as many facts from those pages as it can. Then, the system creates knowledge graphs. Diffbot crawls the web nonstop and rebuilds its knowledge graph every four to five days. Find out more in this interesting article!
» We’re entering the AI twilight zone between narrow and general AI by Gary Grossman on VentureBeat | September 3
The author touches the topic of the “AI Winter”, computing limitations, and narrow and general AI. What can we expect in the future? Read yourself! 😉
» Q&A: Physical scientists turn to deep learning to improve Earth systems modeling by Kathy Kincade, Lawrence Berkeley National Laboratory on Phys.org | September 4
An interesting interview with Karthik Kashinath, a computer scientist and engineer in the Data & Analytics Services Group (DAS) at the National Energy Research Scientific Computing Center (NERSC). An interesting read for all the enthusiasts of Earth and the use of ML in weather, climate, and Earth systems. 🌎
» The Top 5 AI and Machine Learning Trends to Watch Out For in 2021 by Devan Bansal on Techopedia | September 2
The Top AI and Machine Learning Trends for 2️⃣0️⃣2️⃣1️⃣ include advancements in forecasting, healthcare, reinforcement learning, conversational AI, and predictive maintenance. Check out why!
» How Google Maps uses DeepMind’s AI tools to predict your arrival time by James Vincent on The Verge | September 3
Whenever and wherever I travel by car, Google Maps always accurately predicts my arrival time, like, always 🏎 If you’re wondering why sometimes Google tells you to take the (seemingly) longer route, read the article. Concise and interesting!
The article was originally published on Google Blog: Google Maps 101: How AI helps predict traffic and determine routes
» 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 ->