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The hype and benefits of deep spectral modelling


Webinar on

The hype and benefits of deep spectral modelling

25th November 2021 at 3pm (UK time)


Deep learning is emerging as a potential new direction for data modelling. Its applications can be found in almost all scientific fields ranging from agriculture to medicine to astrophysics. In the domain of chemometrics as well an increasing trend towards deep learning is emerging, where particularly for spectral data modelling, deep learning has shown to outperform several state-of-the-art chemometric approaches. Furthermore, for spectral image processing as well, the advanced concept of deep learning such as convolution modelling, semantic segmentation and generative adversarial networks have shown efficient modelling by combining both the spatial and spectral information. On one hand where the deep learning is emerging as a powerful tool, however, on the other hand, its applications are over-hyped in scientific chemometric literature, where usually the comparison of deep learning is performed with simple linear models and deep learning is demonstrated as a winner. This talk aims to bring the attention of the audience towards the potential and hypes of deep learning for spectral data modelling. The key idea is that once the audience is aware of the do and don’ts of deep learning, then they can use deep learning as a complementary tool to chemometrics


This webinar will be presented by Puneet Mishra, Wageningen Food and Biobased Research, The Netherlands

The webinar will last no longer than one hour.

To register please contact Christine.stevenson@strath.ac.uk

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