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

Deep spectral modelling for regression and classification is gaining popularity in the chemometrics domain. A major topic deep learning (DL) of data choice optimization neural network architecture suitable specific task modelling. Although there are several recent research articles already available chemometric domain showing advanced approaches to modelling, currently, a lack hands-on tutorial this space that supply non-expert user with practical tools learn implement DL methodologies aimed at data. Hence, article aims reducing gap between community implementation models daily usage. This supplies quick introduction state-of-the-art related concepts presents set hyperparameters’ optimization. To end, shows two examples on how optimize tasks. The implemented python Tensorflow complete code supplied form complementary notebooks.