Using Deep Learning to Transform and Analyse NMR Data

It will be shown how deep neural networks (DNNs) can be trained for reconstruction of sparsely sampled NMR spectra and for virtual homonuclear decoupling. Another area covered in the talk will be autonomous analysis of chemical exchange saturation transfer (CEST) data, where the Trained DNN accurately predicts both the chemical shifts and the uncertainties associated with these.

9:00AM California or 12:00 am Boston or 5:00 PM Paris or 9:30 PM Delhi