J pattabiraman (26 risultati)

Condizione: New.

Condizione: New. First Edition NO-PA16APR2015-KAP.

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Paperback or Softback. Condizione: New. Strength of Materials. Book.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: Brand New. 492 pages. 7.50x0.99x9.25 inches. In Stock.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: New.

Paperback. Condizione: New.

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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Ever since his student days at IIT Madras and during his 43 years of his professional career that spanned Engineering Consulting and Industry, Research and Teaching, the author has been intensely involved with the subjects Strength of Materials, Design and Analysis of Mechanical components and Systems and Vibrations. Inspired with a passion for these subjects, the author felt the need, to establish a close link between'Strength of Materials' and 'Design Engineering' and that 'Strength of Materia.…

Taschenbuch. Condizione: Neu. Strength of Materials | J. Pattabiraman | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | MJP Publishers | EAN 9788180941566 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: 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.…

Taschenbuch. Condizione: 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.…