Neural networks (centre) can be used to investigate phase transitions, for instance of magnetic materials (arrows). (Illustration: Department of Physics, University of Basel)
Neural networks ( centre ) can be used to investigate phase transitions, for instance of magnetic materials (arrows). (Illustration: Department of Physics, University of Basel) Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future. September 2022 Neural networks are based on the principle of operation of the brain. Such computer algorithms learn to solve problems through repeated training and can, for example, distinguish objects or process spoken language.
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