| 書目名稱 | Deep Learning: Concepts and Architectures |
| 編輯 | Witold Pedrycz,Shyi-Ming Chen |
| 視頻video | http://file.papertrans.cn/265/264643/264643.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 |  |
| 描述 | This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting. |
| 出版日期 | Book 2020 |
| 關(guān)鍵詞 | Computational Intelligence; Machine Learning; Computer Vision; Natural Language Processing; Deep Learnin |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-31756-0 |
| isbn_softcover | 978-3-030-31758-4 |
| isbn_ebook | 978-3-030-31756-0Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer Nature Switzerland AG 2020 |
|Archiver|手機版|小黑屋|
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