Ahmed sameh (276 resultados)

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New.

Condición: New. In English.

Condición: New.

Hardcover. Condición: Very Good. Kuck, David J.; Lawrie, Duncan H.; and Sameh, Ahmed H. [Editors] : High Speed Computer and Algorithm Organization. New York : Academic Press Inc, 1977. No additional printings listed. Octavo (8vo), 9.25"/23.3 cm h. Bound in publisher's gray cloth, maroon lettering to spine and front board. Plain white endpapers. Xii, 468. Condition notes: a solid copy: former owner's name to head ffep, else unmarked. Binding sound and square. A very nice copy indeed. We personally inspect every book we offer.…

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: New. Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process.…

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New. Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process.…

Condición: New.

paperback. Condición: New.

Condición: As New. Unread book in perfect condition.

Kartoniert / Broschiert. Condición: New.

Condición: New. In English.

Condición: New. pp. 88.

Condición: New.

Hardcover. First Ed, unstated. First Ed, unstated. EX-LIBRARY, withdrawn, showing the usual marks and designations. Else this book is in Fine condition: Binding square and secure; text clean. Virtually 'As New', showing no flaws, other than the Library markings. NOT a Remainder or Book-Club. 8vo. 203pp. Hardback: No DJ 'as issued'. …

Condición: Very Good.

Hardcover. Condición: Fine. 9401771871 Like New; Hardcover; Close to new condition; Covers are still glossy with straight" edge-corners; Unblemished textblock edges; The endpapers and all text pages are bright and unmarked; Binding is tight with a straight spine; This book will be stored and delivered in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); Blue and gray covers with title in blue lettering; 2015, Springer-Verlag Publishing; 473 pages; "Parallelism in Matrix Computations (Scientific Computation)," by Efstratios Gallopoulos, et al.…

Condición: New. KlappentextrnrnProvide a short description in simple, non-technical language that aptly expresses the book s scope and theme. This study explores policy options based on evidence from international experience that will help Bangladesh improve th.

Condición: New.

Hardcover. Condición: As New. 1. Auflage. Unread, like new. Immediately dispatched from Germany.

Paperback. Condición: Brand New. 400 pages. 9.19x7.00x9.19 inches. In Stock.

Paperback. Condición: Brand New. 156 pages. 8.66x5.91x0.36 inches. In Stock.

Condición: New. 1st edition NO-PA16APR2015-KAP.

Condición: New.

Paperback. Condición: New. Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building the E2E graph learning pipeline in a world of dynamic and evolving graphs.Understand the importance of graph learning for boosting enterprise-grade applicationsNavigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelinesUse traditional and advanced graph learning techniques to tackle graph use casesUse and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applicationsDesign and implement a graph learning algorithm using publicly available and syntactic dataApply privacy-preserved techniques to the graph learning process.…