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Titlebook: Handbook of Deep Learning Applications; Valentina Emilia Balas,Sanjiban Sekhar Roy,Pijush Book 2019 Springer Nature Switzerland AG 2019 D

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發(fā)表于 2025-3-28 17:28:02 | 只看該作者
42#
發(fā)表于 2025-3-28 21:16:21 | 只看該作者
Die Basics: Begriffe der Stromwirtschaft,he deep learning approach which is attracted much attention in the field of machine learning is given in recent years and an application about semantic image segmentation is carried out in order to help autonomous driving of autonomous vehicles. This application is implemented with Fully Convolution
43#
發(fā)表于 2025-3-28 23:23:42 | 只看該作者
https://doi.org/10.1007/978-3-658-15164-5 The visual features of a surgical video can be used to identify the surgical phases in laparoscopic interventions. Owing to the significant improvement in performance exhibited by convolutional neural networks (CNN) on various challenging tasks like image classification, action recognition etc., th
44#
發(fā)表于 2025-3-29 06:32:07 | 只看該作者
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發(fā)表于 2025-3-29 10:14:27 | 只看該作者
https://doi.org/10.1007/978-3-663-11691-2d autoencoder network cascaded with a softmax layer. The classifier is trained by applying a special training approach, where each layer of the proposed classifier is trained individually and sequentially. The performance of the proposed classifier is compared with a number of representative classif
46#
發(fā)表于 2025-3-29 12:24:13 | 只看該作者
Methodisch-methodologischer Ansatz,ventional learning methods such as the error back-propagation is faced with serious obstacles owing to local minima. The layer-by-layer pre-training method has been recently proposed for training these neural networks and has shown considerable performance. In the pre-training method, the complex pr
47#
發(fā)表于 2025-3-29 16:36:29 | 只看該作者
https://doi.org/10.1007/978-3-7091-1075-1ers have been performing particularly well for multimedia mining tasks such as object or face recognition and Natural Language Processing tasks such as speech recognition and voice commands. This opens up a lot of new possibilities for medical applications. Deep Learners can be used for medical imag
48#
發(fā)表于 2025-3-29 23:31:45 | 只看該作者
,Erratum to: Bahnk?rper und Nebenanlagen,as set new standards in the world of prosthetics, be it hearing aids or prosthetic arms, legs or vision, helping paralyzed or completely locked-in users. Not only can one get a visual imprint of their own brain activity but the future of BCI will make sharing someone else’s experience possible. The
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發(fā)表于 2025-3-30 01:23:44 | 只看該作者
50#
發(fā)表于 2025-3-30 05:12:20 | 只看該作者
Studien zur Kommunikationswissenschaft been made through data mining but there is an increasing research focus on deep learning to exploit the massive improvement in computational power. This chapter presents recent advancements in deep learning research and identifies some remaining challenges as drawn from using deep learning in the a
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