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Using deep learning approaches and convolutional neural networks (CNN) for spectroscopical data (deep chemometrics)
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README.md

README.md

Deep Chemometrics with Data Augmentation

Deep Chemometrics Rising

The repository contains two examples of using convolutional neural networks to model spectroscopical data with data augmentation or EMSC as means to handle the variation in baseline.

The first example show what kind of results are possible using data augmentation: Example with DA

The other example use the same setup, but with EMSC used for baseline correction: Example with EMSC

The approach and background + a comparison with PLS models are further described in https://arxiv.org/pdf/1710.01927 and the blog post https://www.wildcardconsulting.dk/useful-information/deep-chemometrics-deep-learning-for-spectroscopy/

Feel free to leave a comment on the blog post if you find it useful ;-)

Reference

Please cite: https://arxiv.org/abs/1710.01927

Support

Commercial support discontinued. Wildcard pharmaceutical consulting: https://www.wildcardconsulting.dk/useful-information/wildcard-pharmaceutical-consulting-will-be-closed/

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