Space Science with Python - Cassini #4: Machine Learning based calibration

Repository: https://github.com/ThomasAlbin/Astroniz-YT-Tutorials/blob/main/%5BProject%5D-Cassini-CDA/01-Calibration/03_machine_learning_calibration.ipynb After taking a look at the already existing calibration functions, today, we will try to create a first "machine learning toy model" to calibrate CDA. For this purpose, we use Tensorflow and its high-level API keras. In the following videos we will refine our calibration attempt to create a production-ready model for future scientific analysis. Credit - Thumbnail: NASA/JPL-Caltech --- Content 0:00 Introduction 1:32 Coding - Intro 2:21 Coding - Read in the data 3:07 Coding - Plotting the calibration data in 3D 6:59 Coding - Data preparation 9:12 Coding - Machine Learning Training 13:35 Coding - Solution space in 3D 17:06 Summary & Outlook --- How to setup a local dev environment: https://www.youtube.com/watch?v=PWzvRDylbQA --- 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: https://twitter.com/MrAstroThomas Reddit: https://www.reddit.com/user/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