Space Science with Python - Cassini #6: Keras-Tuner
Repository: https://github.com/ThomasAlbin/Astron...
Modeling ML models and training them is a multi-dimensional problem. Last time, we created a simple neural network to create a first iteration of a calibration function.
However, one can do a lot more to create "the perfect" model (though it never really exists). One thing to consider: the distribution of the input data by either down- or upsampling. Here, we use a method called "sample weight".
Our model does not have the most complex data ... however next time we take a look at k-folding and Keras Tuner, to complete this small task. Afterwards we consider more data for calibration purposes and will dive back into our simple "1D" function.
Credit - Thumbnail (Cassini): NASA/JPL-Caltech
Credit - Thumbnail (Saturn): NASA/JPL/Space Science Institute
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Content
0:00 Introduction
1:25 Coding - Read in and split the data
2:44 Coding - Machine Learning Training
8:30 Coding - Best ML Model
9:28 Coding - Metric results
11:20 Outlook
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How to setup a local dev environment: • Space Science with Python - Part 2: S...
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There is a lot to do and to learn and I hope you will join the journey. Meanwhile, if you have questions or ideas, reach out to me via:
Mastodon: https://astrodon.social/@ThomasAlbin
Twitter: / mrastrothomas
Reddit: / mrastrothomas
GitHub: https://github.com/ThomasAlbin
Or drop a comment!
Talk to you later,
Thomas
#space #science #Python #tutorial #datascience #cassini #nasa #saturn #ml #ai #machinelearning #deeplearning