Items related to Recurrent Neural Networks: From Simple to Gated Architecture...
- PublisherSpringer
- Publication date2022
- ISBN 10 3030899284
- ISBN 13 9783030899288
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages141
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Recurrent Neural Networks: From Simple to Gated Architectures
Seller: Book Deals, Tucson, AZ, U.S.A.
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Condition: Fair. Acceptable/Fair condition. Book is worn, but the pages are complete, and the text is legible. Has wear to binding and pages, may be ex-library. 0.79. Seller Inventory # 353-3030899284-acp
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Recurrent Neural Networks: From Simple to Gated Architectures
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Condition: Very Good. Very Good condition. Shows only minor signs of wear, and very minimal markings inside (if any). 0.79. Seller Inventory # 353-3030899284-vrg
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Recurrent Neural Networks: From Simple to Gated Architectures
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Condition: Fine. Like New condition. Great condition, but not exactly fully crisp. The book may have been opened and read, but there are no defects to the book, jacket or pages. 0.79. Seller Inventory # 353-3030899284-lkn
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Recurrent Neural Networks: From Simple to Gated Architectures
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Recurrent Neural Networks
Published by
Springer Nature Switzerland AG, 2022
ISBN 10: 3030899284
ISBN 13: 9783030899288
New
Hardcover
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # S0-9783030899288
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Recurrent Neural Networks: From Simple to Gated Architectures
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Recurrent Neural Networks: From Simple to Gated Architectures
Print on DemandSeller: Revaluation Books, Exeter, United Kingdom
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Hardcover. Condition: Brand New. 141 pages. 9.25x6.10x0.59 inches. In Stock. This item is printed on demand. Seller Inventory # __3030899284
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Recurrent Neural Networks
Published by
Springer International Publishing Jan 2022, 2022
ISBN 10: 3030899284
ISBN 13: 9783030899288
New
Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author's approach enables strategic co-trainingof output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications. 144 pp. Englisch. Seller Inventory # 9783030899288
Quantity: 2 available
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Recurrent Neural Networks
Seller: Books Puddle, New York, NY, U.S.A.
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Condition: New. Seller Inventory # 26394493820
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Recurrent Neural Networks : From Simple to Gated Architectures
Published by
Springer International Publishing, 2022
ISBN 10: 3030899284
ISBN 13: 9783030899288
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author's approach enables strategic co-trainingof output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications. Seller Inventory # 9783030899288
Quantity: 1 available