Neural networking with advanced parallel processing is beginning to take root in a number of markets ranging from predicting earthquakes and hurricanes to parsing MRI image datasets in order to identify and classify tumors.
As this approach gets implemented in more places, it is being customized and parsed in ways that many experts never envisioned. And it is driving new research into how else these kinds of compute architectures can be applied.
Fjodor van Veen, deep learning researcher at The Asimov Institute in the Netherlands, has identified 27 distinct neural net architecture types. (See Fig. 1 below). The differences are largely application-specific.
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