Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks
نویسندگان
چکیده
Through the development of artificial intelligence, some capabilities human beings have been replicated in computers. Among developed models, convolutional neural networks stand out considerably because they make it possible for systems to inherent humans, such as pattern recognition images and signals. However, conventional methods are based on deterministic which cannot express epistemic uncertainty their predictions. The alternative consists probabilistic although these more difficult develop. To address problems related choice network architecture, this article proposes an application that allows user choose desired architecture with trained model given data. This application, named “Graphical User Interface Probabilistic Neural Networks”, develop or use a standard provided data, already adapted implement model. Contrary existing models generic use, pre-trained databases be used transfer learning, approach followed work creates layer by layer, training performed originating specific data question.
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ژورنال
عنوان ژورنال: Signals
سال: 2023
ISSN: ['2624-6120']
DOI: https://doi.org/10.3390/signals4020016