In the interdisciplinary ,,Deep Turb’ project, neural networks are being used to better predict how flows behave. (Photo: Nasa)
In the interdisciplinary ,,Deep Turb' project, neural networks are being used to better predict how flows behave. (Photo: Nasa) - Scientists at TU Ilmenau have succeeded in improving the accuracy of data evaluations for forecasting weather events, among other things, by up to 80 percent. In the scientific publication ,,Flipped Classroom - Effective Teaching for Time Series Forecasting", which was published in October, Prof. Patrick Mäder, head of the department Data-intensive Systems and Visualization, and Philipp Teutsch, research associate at the same department, present their research results on the training of recurrent neural networks. They combined different training methods of machine learning to minimize errors in the evaluation of large data sets. Artificial neural networks have become an indispensable part of our everyday lives: In voice assistants, in translation or simulation software, machine learning technology processes data in a multitude of applications. The principle behind artificial intelligence (AI)-based information evaluation is based on neurons that are interconnected in a network like nodes. They record, process and evaluate information.
TO READ THIS ARTICLE, CREATE YOUR ACCOUNT
And extend your reading, free of charge and with no commitment.
