3 resultados para Deep foundations

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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Regulators and market participants have become increasingly concerned about the Spanish electricity tariff deficit due to its size and the difficulties to control its growth. The deficit can be traced to inefficiencies in market organization and solutions should be designed to mitigate those inefficiencies. Tariff deficits have allowed for the transfer of part of the present costs of electricity services to future consumers, but this situation has reached a limit and a deep revision of regulation in this market cannot be postponed. In general, solutions that interfere with market prices and signals are not appropriate.

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Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.