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--- tutorials [2022/01/12 10:37] – admin
+++ tutorials [2022/04/21 08:42] (current) – admin
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-====== Tutorials related to machine learning ======
+====== ML pipeline: Automated detection and classification of ICMEs ======
-  * We have a [[https://github.com/epn-ml/Tutorial_IWF-ICMEs|tutorial about the ML pipeline]] developed for our [[:science_cases:iwf_science_case|IWF ICME science case]] on our [[https://github.com/epn-ml|GitHub account]]:
+We have a [[https://github.com/epn-ml/Tutorial_IWF-ICMEs|tutorial about the ML pipeline]] developed for our [[:science_cases:iwf_science_case|IWF ICME science case]] on our [[https://github.com/epn-ml|GitHub account]].
-In this tutorial, we will introduce an ML pipeline for the automated detection of interplanetary coronal mass ejections (ICMEs) in solar wind time series data. We will guide the reader through the developed ML code with the help of a sample data set of solar wind time series data from different spacecraft (WIND, STEREO-A and STEREO-B).
+In the tutorial, we will introduce an ML pipeline for the automated detection of interplanetary coronal mass ejections (ICMEs) in solar wind time series data. We will guide the reader through the developed ML code with the help of a sample data set of solar wind time series data from different spacecraft (WIND, STEREO-A and STEREO-B).
 We propose a pipeline using a UNet including residual blocks, squeeze and excitation blocks, Atrous Spatial Pyramidal Pooling (ASPP) and attention blocks, similar to the [[https://arxiv.org/pdf/1911.07067.pdf|ResUNet++]], for the automatic detection of ICMEs. The original model was used for medical image segmentation, while we are dealing with time series and therefore face a slightly different use case.
 Feel free to try out our pipeline and provide feedback about it! :)
-====== Other useful tutorials and links ======
-  * A very nice and comprehensive "Space Science with Python" tutorial: \\ [[https://github.com/ThomasAlbin/SpaceScienceTutorial|https://github.com/ThomasAlbin/SpaceScienceTutorial]]