Excited to be in #AGU20’s tutorial on #MachineLearning for #Geoscience today with over 1200 registered participants from across the world!
First time I'm hearing about the Zarr data format for breaking large datasets into a large number of small binary files that are good for cloud storage. zarr.readthedocs.io/en/stable/
Thanks so much @DJGagneDos for a great intro ML talk at #AGU20 and for sharing a link to your recorded talk/slides as presented to the NCAR ARTIFICIAL INTELLIGENCE FOR EARTH SYSTEM SCIENCE (AI4ESS) Summer School! www2.cisl.ucar.edu/events/summer-…
Some additional really great ML fundamentals resources by way of Ankur Mahesh at Berkeley:
J. Shewchuck's "Concise ML" lecture notes: people.eecs.berkeley.edu/~jrs/jrspapers…
S. Nasiriany, G. Thomas, et al.'s "A Comprehensive Guide to Machine Learning": snasiriany.me/cs189/
J. Shewchuck's "Concise ML" lecture notes: people.eecs.berkeley.edu/~jrs/jrspapers…
S. Nasiriany, G. Thomas, et al.'s "A Comprehensive Guide to Machine Learning": snasiriany.me/cs189/
It was helpful to see the concrete example of using neural nets to emulate the output from the Community Land Model by @katiedagon during the #AGU20 ML tutorial:
Looking forward to seeing your forthcoming paper in …matology-meteorology-oceanography.net
https://twitter.com/katiedagon/status/1338632458544156674?s=20
Looking forward to seeing your forthcoming paper in …matology-meteorology-oceanography.net
Lots of references are being made to prior meetings this year with online talks. One relevant to ML is: slideslive.com/climateinforma…
So many good questions at the #AGU20 ML tutorial, and Karthik Kashinath and the other conveners have been really quick to post relevant articles and blogs that cover a myriad of topics. TowardsDataScience.com seems to come up frequently.
For example, towardsdatascience.com/clearly-explai…
For example, towardsdatascience.com/clearly-explai…
No surprise that under/over-fitting data causes poor performance in machine learning as it does with many aspects of research and life! Another good resource from machinelearningmastery.com mentioned at the ML tutorial.
machinelearningmastery.com/overfitting-an…
machinelearningmastery.com/overfitting-an…
How to choose a metric for evaluating uncertainty in machine learning? Some ideas: cs.adelaide.edu.au/~javen/talk/ML…
That time when you pull up a relevant and well written article on feature selection and reducing the dimensionality of data and you stumble upon a weird (and awesome!) data science competition to not overfit data.
towardsdatascience.com/feature-select…
towardsdatascience.com/feature-select…
Thinking about #AirborneScience applications for object tracking reminds me of the (apocryphal?) stories of grad students of yore having to watch 8-10 hours of forward cam videos per flight to identify each and every time the airplane flew through a cloud. missinglink.ai/guides/compute…
Tapping out early to go make dinner for the kiddos, but really enjoyed this afternoon's #AGU20 #MachineLearning tutorial! Thanks to all the conveners and presenters for an outstanding set of talks, resources, and practical examples! agu.confex.com/agu/fm20/meeti… 
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