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Titlebook: Development and Analysis of Deep Learning Architectures; Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Compu

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書目名稱Development and Analysis of Deep Learning Architectures
編輯Witold Pedrycz,Shyi-Ming Chen
視頻videohttp://file.papertrans.cn/270/269894/269894.mp4
概述Provides a comprehensive and up-to-date overview of deep learning by discussing a range of methodological and algorithmic issues.Addresses implementations and case studies, identifying the best design
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Development and Analysis of Deep Learning Architectures;  Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Compu
描述This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
出版日期Book 2020
關(guān)鍵詞Computational Intelligence; Machine Learning; Computer Vision; Natural Language Processing; Deep Learnin
版次1
doihttps://doi.org/10.1007/978-3-030-31764-5
isbn_softcover978-3-030-31766-9
isbn_ebook978-3-030-31764-5Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Gehetzte Erben, hektische Epigonenecent years. Domain adaptation or transfer learning algorithms alleviate this challenge by transferring relevant knowledge from a source domain to induce a model for a related target domain, where labeled data are scarce. Further, deep learning algorithms are instrumental in learning informative fea
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Joan Kristin Bleicher,Bernhard P?rksenecially on the perception of the environment by camera, Radar, and Lidar sensors and fusion concepts. Camera-based perception includes the detection of road users. Highest detection performance is especially required for detecting vulnerable road users such as pedestrians and bicycle drivers. Here,
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Zusammenfassung des Analytischen Rahmens signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network?(CNN) architectures for baby cry detection, and compare thei
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https://doi.org/10.1007/978-3-031-35096-2rms of attacks are constantly emerging to exploit vulnerabilities in system compromising the security parameters such as Confidentiality, Integrity and Availability (CIA). Injection attacks also termed as False Data Injection Attacks (FDIA) are the complex attacks on the ICS. FDIA affects the data i
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Migration im Kontext des EU-Grenzmanagementsg wireless applications with high degrees of freedom. Deep learning?has a strong potential to overcome this challenge via data-driven solutions and improve the performance of wireless systems?in utilizing limited spectrum resources. In this chapter, we first describe how deep learning?is used to des
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https://doi.org/10.1007/978-3-031-35096-2 spread propaganda, communicate and organize has increased. However, techniques to effectively identify such material are lacking. This chapter explores an approach which can classify any piece of text as belonging to one of four extremist groups: Sunni Islamic, Antifascist Groups, White Nationalist
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