Space Science with Python - AI 1-11: Autoencoder Signal Reconstruction
GitHub Link of today's session: https://github.com/ThomasAlbin/Astron...
The last 3 videos of our asteroid spectra project will focus on an un-supervised method to identify potential asteroid spectra classes from a data scientific perspective. We will not use any labels; but will use them only to check or verify our algorithms' behavior.
Anyway, we start our final un-supervised journey with a deep learning architecture that shall be the fundament for our sessions: Autoencoders.
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How to setup a local dev environment: • Space Science with Python - Part 2: Setup ...
Autoencoders: • Space Science with Python - Concepts 4: Au...
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Content
0:00 Introduction / Autoencoder
3:35 Cell #1 - Import libraries
3:45 Cell #2 - Setting a Seed
4:40 Cell #3 - Reading the data
5:17 Cell #4 - Data Shuffle Split
5:52 Cell #5 - Data scaling with Scikit-Learn
6:56 Cell #6 - Autoencoder building & compilation
12:08 Cell #7 - Model training
12:30 Cell #8 - Loss function
13:30 Cell #9 - Signal reconstruction
16:07 Summary & Outlook
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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:
Twitter: / mrastrothomas
Reddit: / mrastrothomas
GitHub: https://github.com/ThomasAlbin
Or drop a comment!
Talk to you later,
Thomas