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US$ 23.08
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Add to basketPaperback. Condition: Brand New. 188 pages. 8.00x5.00x0.43 inches. In Stock.
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Prenatal exposure to progesterone suppresses reproduction in male mice
Language: English
Published by Editorial Academica Espanola, 2011
ISBN 10: 3846501042 ISBN 13: 9783846501047
Condition: New. pp. 148.
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Prenatal exposure to progesterone suppresses reproduction in male mice: Partial recovery of reproduction by testosterone
Language: English
Published by LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846501042 ISBN 13: 9783846501047
US$ 160.29
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Scalable and Distributed Machine Learning and Deep Learning Patterns
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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US$ 286.13
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Scalable and Distributed Machine Learning and Deep Learning Patterns
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Scalable and Distributed Machine Learning and Deep Learning Patterns
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Scalable and Distributed Machine Learning and Deep Learning Patterns (Advances in Computational Intelligence and Robotics)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Prenatal exposure to progesterone suppresses reproduction in male mice
Language: English
Published by LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3846501042 ISBN 13: 9783846501047
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Reddy P. SreenivasulaProf. Sreenivasula Reddy, Department of Zoology,S.V. University, Tirupati, India received post-doctoraltraining at Penn. State University, USA and Universityof California at Davis, USA. He is a recipient ofRockef.
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Prenatal exposure to progesterone suppresses reproduction in male mice
Language: English
Published by Editorial Academica Espanola, 2011
ISBN 10: 3846501042 ISBN 13: 9783846501047
US$ 105.34
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Add to basketCondition: New. Print on Demand pp. 148 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.
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Prenatal exposure to progesterone suppresses reproduction in male mice
Language: English
Published by Editorial Academica Espanola, 2011
ISBN 10: 3846501042 ISBN 13: 9783846501047
Condition: New. PRINT ON DEMAND pp. 148.
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Scalable and Distributed Machine Learning and Deep Learning Patterns
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Scalable and Distributed Machine Learning and Deep Learning Patterns
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Taschenbuch. Condition: Neu. Scalable and Distributed Machine Learning and Deep Learning Patterns | J. Joshua Thomas (u. a.) | Taschenbuch | Englisch | 2023 | IGI Global | EAN 9798369304457 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
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Condition: New. PRINT ON DEMAND.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
