  The use of deep learning is facilitating a wide range of data processing
tasks in many areas. The analysis of fusion data is no exception, since there
is a need to process large amounts of data collected from the diagnostic
systems attached to a fusion device. Fusion data involves images and time
series, and are a natural candidate for the use of convolutional and recurrent
neural networks. In this work, we describe how CNNs can be used to reconstruct
the plasma radiation profile, and we discuss the potential of using RNNs for
disruption prediction based on the same input data. Both approaches have been
applied at JET using data from a multi-channel diagnostic system. Similar
approaches can be applied to other fusion devices and diagnostics.
