Name: Model Optimization Methods for Efficient and Edge ai: Federated Learning Architectures, Frameworks and Applications (en Inglés)
Price: 160.77 USD
Availability: InStock
Author: Pethuru Raj Chelliah; Amir Masoud Rahmani; Robert Colby; Gayathri Nagasubramanian; Sunku Ranganath
ISBN: 9781394219216

portada Model Optimization Methods for Efficient and Edge ai: Federated Learning Architectures, Frameworks and Applications (en Inglés)

Model Optimization Methods for Efficient and Edge ai: Federated Learning Architectures, Frameworks and Applications (en Inglés)

Formato

Libro Físico

Editorial

Wiley-Ieee Press

Autor

Pethuru Raj Chelliah; Amir Masoud Rahmani; Robert Colby; Gayathri Nagasubramanian; Sunku Ranganath

Idioma

Inglés

N° páginas

528

Encuadernación

Tapa Dura

ISBN13

9781394219216

N° edición

Model Optimization Methods for Efficient and Edge ai: Federated Learning Architectures, Frameworks and Applications (en Inglés)

Pethuru Raj Chelliah; Amir Masoud Rahmani; Robert Colby; Gayathri Nagasubramanian; Sunku Ranganath (Autor) · Wiley-Ieee Press · Tapa Dura

Libro Nuevo Importado

Envío: 18 a 25 días háb.

$ 292.32$ 160.77

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 51 unidades

$ 160.77

Llega entre el 07 Sep y el 18 Sep a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Model Optimization Methods for Efficient and Edge ai: Federated Learning Architectures, Frameworks and Applications (en Inglés)"

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