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Titlebook: Deep Learning: Fundamentals, Theory and Applications; Kaizhu Huang,Amir Hussain,Rui Zhang Book 2019 Springer Nature Switzerland AG 2019 Ne

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發(fā)表于 2025-3-21 19:40:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Deep Learning: Fundamentals, Theory and Applications
編輯Kaizhu Huang,Amir Hussain,Rui Zhang
視頻videohttp://file.papertrans.cn/265/264645/264645.mp4
概述Provides thorough background of deep learning.Introduces widely-used learning architectures and algorithms.Includes new theory and applications of deep learning
叢書(shū)名稱Cognitive Computation Trends
圖書(shū)封面Titlebook: Deep Learning: Fundamentals, Theory and Applications; Kaizhu Huang,Amir Hussain,Rui Zhang Book 2019 Springer Nature Switzerland AG 2019 Ne
描述.The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. .Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field.. .This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in stud
出版日期Book 2019
關(guān)鍵詞Neural networks; Deep representation; Learning; Optimization; Artificial intelligence; Cognitively-inspir
版次1
doihttps://doi.org/10.1007/978-3-030-06073-2
isbn_ebook978-3-030-06073-2Series ISSN 2524-5341 Series E-ISSN 2524-535X
issn_series 2524-5341
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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https://doi.org/10.1007/978-3-030-06073-2Neural networks; Deep representation; Learning; Optimization; Artificial intelligence; Cognitively-inspir
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Springer Nature Switzerland AG 2019
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Politische Kultur und Sprache im Umbruchtraining data. The other is how to effectively encode both the current signal segment and the contextual dependency. Both needs many human efforts. Motivated to relieve such issues, this chapter presents a systematic investigation on architecture design strategies for recurrent neural networks in tw
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Anpassung der ostdeutschen Wirtschaftgence and computer science. Deep learning technologies have been well developed and applied in this area. However, the literature still lacks a succinct survey, which would allow readers to get a quick understanding of (1) how the deep learning technologies apply to NLP and (2) what the promising ap
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Amerikanisierung vs. Modernisierung as humans do. It becomes a necessity in the Internet age and big data era. From fundamental research to sophisticated applications, natural language processing includes many tasks, such as lexical analysis, syntactic and semantic parsing, discourse analysis, text classification, sentiment analysis,
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Kaizhu Huang,Amir Hussain,Rui ZhangProvides thorough background of deep learning.Introduces widely-used learning architectures and algorithms.Includes new theory and applications of deep learning
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