Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering

We review the field of deep learning and artificial intelligence methods applied to Raman and SERS sensors. It covers the basics of a series of deep learning and artificial intelligence, guides the reader in using these methods and proposes a number of examples where these methods were applied in Raman and SERS sensors. Machine learning is shaping up our lives in many ways. In analytical sciences, machine learning provides an unprecedented opportunity to extract information from complex or big datasets in chromatography, mass spectrometry, NMR, and spectroscopy, among others. This is especially the case in Raman and surface-enhanced Raman scattering (SERS) techniques where vibrational spectra of complex chemical mixtures are acquired as large datasets for the analysis or imaging of chemical systems.