Titlebook: Innovationen und Innovationspotenziale im ?ffentlich-rechtlichen Medienjournalismus; Steffen Grütjen Book 2024 Der/die Herausgeber bzw. de

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ented conditions, without requiring subject-specific data or training. (c) Unlike previous work, our swapping is robust enough to allow for extensive quantitative tests. To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure how intra- and inter-subject face swapping?affect fa

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pects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these framework

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Steffen Grütjentanding of advanced neural networks including ConvNets and S.?Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on compu

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Steffen Grütjentanding of advanced neural networks including ConvNets and S.?Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on compu

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Steffen GrütjenDeep Learning, which is being suggested by researchers as a .About this book..Discover more insight about deep learning algorithms with Swift for TensorFlow. The Swift language was designed by Apple for optimized performance and development whereas TensorFlow library was designed by Google for advan

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Steffen Grütjenults of the catenary detection.Adopts and improves the advan.This book focuses on the deep learning technologies and their applications in the catenary detection of high-speed railways. As the only source of power for high-speed trains, the catenary‘s service performance directly affects the safe op

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