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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. https://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! https://www2.cisl.ucar.edu/events/summer-school/ai4ess/2020/artificial-intelligence-earth-system-science-ai4ess-summer-school
Some additional really great ML fundamentals resources by way of Ankur Mahesh at Berkeley:
J. Shewchuck's "Concise ML" lecture notes: https://people.eecs.berkeley.edu/~jrs/jrspapers.html#mach
S. Nasiriany, G. Thomas, et al.'s "A Comprehensive Guide to Machine Learning": http://snasiriany.me/cs189/
J. Shewchuck's "Concise ML" lecture notes: https://people.eecs.berkeley.edu/~jrs/jrspapers.html#mach
S. Nasiriany, G. Thomas, et al.'s "A Comprehensive Guide to Machine Learning": http://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: https://twitter.com/katiedagon/status/1338632458544156674?s=20
Looking forward to seeing your forthcoming paper in …https://advances-statistical-climatology-meteorology-oceanography.net/
Looking forward to seeing your forthcoming paper in …https://advances-statistical-climatology-meteorology-oceanography.net/
Lots of references are being made to prior meetings this year with online talks. One relevant to ML is: https://slideslive.com/climateinformatics/climate-informatics-2020
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. http://TowardsDataScience.com seems to come up frequently.
For example, https://towardsdatascience.com/clearly-explained-what-why-and-how-of-feature-scaling-normalization-standardization-e9207042d971
For example, https://towardsdatascience.com/clearly-explained-what-why-and-how-of-feature-scaling-normalization-standardization-e9207042d971
and who doesn't love a #CrashCourse!? https://developers.google.com/machine-learning/crash-course
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 https://machinelearningmastery.com/ mentioned at the ML tutorial. https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
How to choose a metric for evaluating uncertainty in machine learning? Some ideas: https://cs.adelaide.edu.au/~javen/talk/ML05_Uncertainty_in_DL.pdf
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. https://towardsdatascience.com/feature-selection-and-dimensionality-reduction-f488d1a035de
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. https://missinglink.ai/guides/computer-vision/object-tracking-deep-learning/
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! https://agu.confex.com/agu/fm20/meetingapp.cgi/Session/105849
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