RosenPy: An open source Python framework for complex-valued neural networks
Deep learning is an essential artificial intelligence tool broadly used in engineering, physics, data science, biology, healthcare, agribusiness, finance, and many other areas. Current Python frameworks for deep learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, only solve real-domain p...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711024002954 |
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| Summary: | Deep learning is an essential artificial intelligence tool broadly used in engineering, physics, data science, biology, healthcare, agribusiness, finance, and many other areas. Current Python frameworks for deep learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, only solve real-domain problems, representing a considerable part of real-world applications but not all. For instance, complex-valued signals are essential for current and future technologies in telecommunications. Thus far, numerous works employing real-valued neural networks adapted to complex-domain processing, end up generating sub-optimal results. To fulfill this demand, this article presents RosenPy, an open-source framework in Python for complex-valued neural networks. |
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| ISSN: | 2352-7110 |