Deep Learning Theoretical Chapter for Chemometrician

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


Deep learning gained a lot of traction in the machine learning community. The performance of these models often surpasses other more classical approaches. Deep learning detractors often point to the lack of interpretability of the complex neural networks and to the, sometimes unrealistic, amount of data needed to train them properly. This chapter provides a basic layout of the inner working of deep learning models and links then to classical neural networks. Furthermore, we review the current and potential application of this approach in the field of chemometrics.
Original languageEnglish
Title of host publicationComprehensive Chemometrics
Subtitle of host publicationChemical and Biochemical Data Analysis
EditorsSteven Brown, Roma Tauler, Beata Walczak
PublisherElsevier BV
Number of pages17
ISBN (Electronic)9780444641663
ISBN (Print)9780444641656
Publication statusPublished - 2020

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