Articoli correlati a Model Optimization Methods for Efficient and Edge Ai:...
Model Optimization Methods for Efficient and Edge Ai: Federated Learning Architectures, Frameworks and Applications - Rilegato
Sinossi
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications
Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more.
The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT).
Other topics covered include:
- Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems
- Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers
- Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced
- Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data
- Overcoming cyberattacks on mission-critical software systems by leveraging federated learning
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
Pethuru Raj Chelliah, PhD, is the Chief Architect of the Edge AI Division of Reliance Jio Platforms Ltd. in Bangalore, India.
Amir Masoud Rahmani, PhD, is an artificial intelligence faculty member at the National Yunlin University of Science and Technology, Taiwan.
Robert Colby is a Principal Engineer in IT Infrastructure responsible for Manufacturing Network Architecture and IoT Infrastructure at Intel Corporation.
Gayathri Nagasubramanian, PhD, is an Assistant Professor with the Department of Computer Science and Engineering at GITAM University in Bengaluru, India.
Sunku Ranganath is a Principal Product Manager for Edge Infrastructure Services at Equinix.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreIEEE
- Data di pubblicazione2024
- ISBN 10 1394219210
- ISBN 13 9781394219216
- RilegaturaCopertina rigida
- LinguaInglese
- Numero di pagine432
- Contatto del produttorenon disponibile
Compra nuovo
Visualizza questo articolo
EUR 134,32
EUR 6,24 per la spedizione da Regno Unito a Italia
Destinazione, tempi e costiRisultati della ricerca per Model Optimization Methods for Efficient and Edge Ai:...
Foto dell'editore
Model Optimization Methods for Efficient and Edge AI
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
Valutazione del venditore 4 su 5 stelle
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo FW-9781394219216
Quantità: 15 disponibili
Immagini fornite dal venditore
Model Optimization Methods for Efficient and Edge Ai : Federated Learning Architectures, Frameworks and Applications
Da: GreatBookPrices, Columbia, MD, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: As New. Unread book in perfect condition. Codice articolo 45832993
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Model Optimization Methods for Efficient and Edge Ai : Federated Learning Architectures, Frameworks and Applications
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo 45832993-n
Quantità: Più di 20 disponibili
Foto dell'editore
Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications
Da: Ria Christie Collections, Uxbridge, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: New. In. Codice articolo ria9781394219216_new
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Model Optimization Methods for Efficient and Edge Ai : Federated Learning Architectures, Frameworks and Applications
Da: GreatBookPrices, Columbia, MD, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo 45832993-n
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Model Optimization Methods for Efficient and Edge Ai : Federated Learning Architectures, Frameworks and Applications
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Valutazione del venditore 5 su 5 stelle
Condizione: As New. Unread book in perfect condition. Codice articolo 45832993
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Model Optimization Methods for Efficient and Edge AI : Federated Learning Architectures, Frameworks and Applications
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Buch. Condizione: Neu. Neuware - Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications. Codice articolo 9781394219216
Quantità: 2 disponibili
Foto dell'editore
Model Optimization Methods for Efficient and Edge AI (Hardcover)
Da: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: new. Hardcover. Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problemsGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablersCompressing AI models so that computational, memory, storage, and network requirements can be substantially reducedAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous dataOvercoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781394219216
Quantità: 1 disponibili
Foto dell'editore
Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications
Da: Books Puddle, New York, NY, U.S.A.
Valutazione del venditore 4 su 5 stelle
Condizione: New. Codice articolo 26401152585
Quantità: 3 disponibili
Foto dell'editore
Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications
Print on DemandDa: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: Brand New. 432 pages. 7.00x0.94x10.00 inches. In Stock. This item is printed on demand. Codice articolo __1394219210
Quantità: 2 disponibili