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- W4213425892 abstract "Deep spectral modelling for regression and classification is gaining popularity in the chemometrics domain. A major topic in the deep learning (DL) modelling of spectral data is the choice and optimization of the deep neural network architecture suitable for the specific task of spectral modelling. Although there are several recent research articles already available in the chemometric domain showing advanced approaches to deep spectral modelling, currently, there is a lack of hands-on tutorial articles in this space that supply the non-expert user with practical tools to learn and implement advanced DL optimization methodologies aimed at spectral data. Hence, this tutorial article aims at reducing the gap between the non-expert user of DL in the chemometric community and the implementation of DL models for daily usage. This tutorial supplies a quick introduction to the state-of-the-art deep spectral modelling and related DL concepts and presents a set of methodologies aimed at DL hyperparameters’ optimization. To this end, this tutorial shows two practical examples on how to implement and optimize two DL models for spectral regression and classification tasks. The models are implemented in python and Tensorflow and the complete code is supplied in the form of two complementary notebooks." @default.
- W4213425892 created "2022-02-25" @default.
- W4213425892 creator A5023012999 @default.
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- W4213425892 date "2022-04-01" @default.
- W4213425892 modified "2023-10-11" @default.
- W4213425892 title "A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks" @default.
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- W4213425892 doi "https://doi.org/10.1016/j.chemolab.2022.104520" @default.
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