A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks

Autor: Dário Passos, Given Names Deactivated Family Name Deactivated
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Chemometrics and Intelligent Laboratory Systems, 223
Chemometrics and Intelligent Laboratory Systems 223 (2022)
ISSN: 0169-7439
Popis: 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 a spectral data. Hence, this tutorial article aims a 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 a 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. info:eu-repo/semantics/publishedVersion
Databáze: OpenAIRE
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