Robert Colby – författare
1 950 kr
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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 learningWritten 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.
1 605 kr
Skickas inom 5-8 vardagar
1 950 kr
Läs direkt efter köp
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 learningWritten 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.
140 kr
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456 kr
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245 kr
Skickas inom 5-8 vardagar
57 kr
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57 kr
Läs direkt efter köp
57 kr
Läs direkt efter köp
57 kr
Läs direkt efter köp
58 kr
Läs direkt efter köp
57 kr
Läs direkt efter köp
57 kr
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And then there were five . . .
Howie was a millionaire and he and Andrea were planning to be married. But he never got to walk to the altar - he was murdered first.All five of Andrea’s lovers were suspects - Doug, Jeff, Ralph, Mark, and Bud. And the motive was clear. But only Andrea knew what impelled her to take her life in her hands and spend one night with each of them after the murder . . .57 kr
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57 kr
Läs direkt efter köp
57 kr
Läs direkt efter köp
57 kr
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198 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar
192 kr
Skickas inom 5-8 vardagar