V Umadevi (121 results)

Condition: New. pp. ix + 132 Illus.

Condition: New. pp. ix + 132.

Condition: New. pp. ix + 132.

Paperback or Softback. Condition: New. Computer Networks. Book.

Contents 1 Introduction 2 Review of literature 3 Research design 4 Results and discussions 5 Findings and conclusion BibliographyThis book illustrates the standing of women in the ICT sector across the globe If adopts the mapping technique to landscape the position of women both in the developed and developing nations and also the barriers they have encountered within the industries 132 pp.…

Condition: New.

Condition: New.

Condition: New. pp. xii + 132.

Condition: New. pp. xii + 132 Illus.

Condition: New. pp. xii + 132.

Condition: New.

Condition: New.

Condition: New. In English.

Condition: New. pp. 140.

Taschenbuch. Condition: Neu. Real-Time Embedded Application and Techniques | Worst Case Response Time in CAN and Bluetooth | V. Umadevi Chezhian | Taschenbuch | 140 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844325898 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Condition: New. Print on Demand.

Taschenbuch. Condition: Neu. CYBER SECURITY | Value Added Course | V. Umadevi (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208430757 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Condition: New. Print on Demand.

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

Condition: New.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. In English.

Hardback. Condition: New. This book discusses various partitioning strategies tailored for traditional machine learning algorithms. It examines how data can be divided efficiently to enhance the performance and scalability of classic machine learning models. It explores how partitioning methods can be applied to neural networks and other deep learning architectures and describes various ways to accelerate training, reduce memory consumption, and enhance overall efficiency.Graphs are prevalent in various AI domains. This book is specifically designed for graph data structures using partitioning techniques and also explores insights into optimizing graph algorithms and analytics. With the explosion of data, efficient partitioning becomes crucial for processing large datasets. This book discusses various partitioning techniques that enable effective management and analysis of big data, enhancing speed and resource utilization. Edge computing demands resource-efficient strategies. It examines partitioning methods tailored for edge devices, enabling AI capabilities at the edge while addressing resource. This book showcases how partitioning techniques have been successfully applied across various AI domains. It demonstrates real-world scenarios where partitioning optimizes AI algorithms and systems.By bridging the gap between theory and practical applications, this book intends to equip researchers, practitioners, and students with invaluable insights into harnessing partitioning for optimizing AI-driven systems, data processing, and problem-solving strategies. It describes the various advantages and disadvantages of partitioning techniques. This book is a vital resource, illuminating the path towards unlocking the full potential of partitioning in shaping the future of AI technologies.…

Hardcover. Condition: Brand New. 120 pages. 9.18x6.12x9.21 inches. In Stock.

Hardback. Condition: New. This book discusses various partitioning strategies tailored for traditional machine learning algorithms. It examines how data can be divided efficiently to enhance the performance and scalability of classic machine learning models. It explores how partitioning methods can be applied to neural networks and other deep learning architectures and describes various ways to accelerate training, reduce memory consumption, and enhance overall efficiency.Graphs are prevalent in various AI domains. This book is specifically designed for graph data structures using partitioning techniques and also explores insights into optimizing graph algorithms and analytics. With the explosion of data, efficient partitioning becomes crucial for processing large datasets. This book discusses various partitioning techniques that enable effective management and analysis of big data, enhancing speed and resource utilization. Edge computing demands resource-efficient strategies. It examines partitioning methods tailored for edge devices, enabling AI capabilities at the edge while addressing resource. This book showcases how partitioning techniques have been successfully applied across various AI domains. It demonstrates real-world scenarios where partitioning optimizes AI algorithms and systems.By bridging the gap between theory and practical applications, this book intends to equip researchers, practitioners, and students with invaluable insights into harnessing partitioning for optimizing AI-driven systems, data processing, and problem-solving strategies. It describes the various advantages and disadvantages of partitioning techniques. This book is a vital resource, illuminating the path towards unlocking the full potential of partitioning in shaping the future of AI technologies.…

Condition: New. PRINT ON DEMAND.