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

Passos, Dário; Mishra, Puneet

Publication

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

2022Journal article

dc.contributor.author	Passos, Dário
dc.contributor.author	Mishra, Puneet
dc.date.accessioned	2022-07-25T11:25:39Z
dc.date.available	2022-07-25T11:25:39Z
dc.date.issued	2022
dc.description.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 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.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.doi	10.1016/j.chemolab.2022.104520	pt_PT
dc.identifier.uri	http://hdl.handle.net/10400.1/18083
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Elsevier	pt_PT
dc.relation	Center for Electronics, Optoelectronics and Telecommunications
dc.relation	Center for Electronics, Optoelectronics and Telecommunications
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Deep learning	pt_PT
dc.subject	Spectroscopy	pt_PT
dc.subject	Chemometrics	pt_PT
dc.subject	Predictive modelling	pt_PT
dc.subject	Optimization	pt_PT
dc.title	A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.awardTitle	Center for Electronics, Optoelectronics and Telecommunications
oaire.awardTitle	Center for Electronics, Optoelectronics and Telecommunications
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00631%2F2020/PT
oaire.citation.startPage	104520	pt_PT
oaire.citation.title	Chemometrics and Intelligent Laboratory Systems	pt_PT
oaire.citation.volume	223	pt_PT
oaire.fundingStream	6817 - DCRRNI ID
oaire.fundingStream	6817 - DCRRNI ID
person.familyName	Passos
person.givenName	Dário
person.identifier	324764
person.identifier.ciencia-id	3D13-C289-0595
person.identifier.orcid	0000-0002-5345-5119
person.identifier.scopus-author-id	21743737200
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.name	Fundação para a Ciência e a Tecnologia
project.funder.name	Fundação para a Ciência e a Tecnologia
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
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