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This Week in Machine Learning: Earthquakes, Music, and More​

Every day brings new opportunities. A week brings even more. To make sure you’re getting the most out of it, we’ve gathered a list of trending articles of the week – everything about Data Science, AI, tech and machine learning. 

Let’s see what was happening in machine learning over the past week.

Here goes a dose of the latest news, discoveries, and inspiring stories. There is something for everyone. Enjoy your read!

Weekly Roundup: February 24th – March 2nd

> How to use machine learning (if you can’t code) to help your keyword research by Andy Chadwick | Search Engine Land, February 28

Here’s an easy way to categorize 100k keywords in less than a few hours of actual working time for SEO and machine learning lovers.

> Machine learning picks out hidden vibrations from earthquake data by Jennifer Chu | MIT News Office, February 28

A fascinating article on how MIT researchers have used a neural network to identify low-frequency seismic waves hidden in earthquake data. The technique may help scientists more accurately map the Earth’s interior.

Interview with fast.ai hero: Radek Osmulski | Fast.ai, Learning to Learn | Machine Learning, Kaggle & Blogging by Sanyam Bhutani CTDS.show (Chai Time Data Science) March 1

If you want to learn interesting facts about Fast.ai, tune in and enjoy the show!

> AI and Machine Learning: How They Are Changing the Content Industry by Alice Jones on | cloud academy, February 28

For those of you who work in content marketing, and write. Read how it affects your work.

> Making Sense of Sound: What Does Machine Learning Mean for Music? by Alex Paretski | datanami.com February 28

Machine learning and its impact on music. Can AI create music? The pros, cons, and limitations.

> Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer by Adam Roberts, Staff Software Engineer and Colin Raffel, Senior Research Scientist, Google Research | Google AI Blog, February 24

Google’s insights from their latest work on NLP.

> KDnuggets™ News of the week with top stories and tweets of the past week, plus opinions, tutorials, events, webinars, meetings, and jobs

> Don’t forget about 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!


READ NEXT

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 ->

Best Practices for Dealing with Concept Drift

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How to Keep Track of Deep Learning Experiments in Notebooks

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