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9780443333620 - Fundamentals of Cost-efficient Ai: in Healthcare and Biomedicine by Kumar (14 results)
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Fundamentals of Cost-efficient Ai : In Healthcare and Biomedicine
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 166.34
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
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Condition: As New. Unread book in perfect condition.
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Fundamentals of Cost-efficient Ai : In Healthcare and Biomedicine
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 192.94
£ 15 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Condition: New. Covers state-of-the-art techniques, including LoRA, PEFT, diffusion models, RAG, Flash Attention, and MoE architecturesExplains methods for model compression, quantization, pruning, and knowledge distillation with practical examples.
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Paperback. Condition: Brand New. 330 pages. 9.25x7.50x9.25 inches. In Stock.
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Fundamentals of Cost-Efficient AI : In Healthcare and Biomedicine
Language: English
Published by Elsevier Science Publishing Co Inc Dez 2025, 2025
ISBN 10: 0443333629 ISBN 13: 9780443333620
Taschenbuch. Condition: Neu. Neuware - Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine provides a comprehensive yet accessible introduction to the principles of designing, training, and deploying efficient artificial intelligence systems. It explains the theory behind cost-aware machine learning and data mining and examines methods across deep learning, graph neural networks (GNNs), transformer architectures, diffusion models, reinforcement learning, and knowledge distillation.The book covers fine-tuning and compression techniques such as low-rank adaptation (LoRA), parameter-efficient fine-tuning (PEFT), adapter-based tuning, pruning, and quantization. It also explores inference acceleration through Flash Attention, prefill optimization, and speculative decoding, and explains how mixture-of-experts (MoE) architectures can scale models efficiently across GPUs and edge devices.To build a strong conceptual understanding, the text introduces fundamentals of GPU architecture, matrix multiplication, memory hierarchies, and parallelization strategies, helping readers develop an intuition for optimizing training and inference pipelines.While applicable across domains, the book places special emphasis on healthcare and biomedicine, where efficient AI can reduce costs and improve diagnostics, precision medicine, and clinical decision support. Real-world case studies and interviews with experts from organizations such as Google and Microsoft provide practical insights into building scalable healthcare AI systems. Aimed at graduate students, researchers, clinicians, biomedical engineers, data scientists, and AI practitioners, this book bridges algorithmic principles with applied implementation.
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Condition: new. Questo è un articolo print on demand.
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Fundamentals of Cost-Efficient AI (Paperback)
Language: English
Published by Elsevier Science Publishing Co Inc, San Diego, 2025
ISBN 10: 0443333629 ISBN 13: 9780443333620
Paperback. Condition: new. Paperback. Efficient cost aware Artificial Intelligence in healthcare and biomedicine This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condition: Brand New. 330 pages. 9.25x7.50x9.25 inches. In Stock. This item is printed on demand.
-
Fundamentals of Cost-Efficient AI (Paperback)
Language: English
Published by Elsevier Science Publishing Co Inc, San Diego, 2025
ISBN 10: 0443333629 ISBN 13: 9780443333620
Paperback. Condition: new. Paperback. Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine provides a comprehensive yet accessible introduction to the principles of designing, training, and deploying efficient artificial intelligence systems. It explains the theory behind cost-aware machine learning and data mining and examines methods across deep learning, graph neural networks (GNNs), transformer architectures, diffusion models, reinforcement learning, and knowledge distillation.The book covers fine-tuning and compression techniques such as low-rank adaptation (LoRA), parameter-efficient fine-tuning (PEFT), adapter-based tuning, pruning, and quantization. It also explores inference acceleration through Flash Attention, prefill optimization, and speculative decoding, and explains how mixture-of-experts (MoE) architectures can scale models efficiently across GPUs and edge devices.To build a strong conceptual understanding, the text introduces fundamentals of GPU architecture, matrix multiplication, memory hierarchies, and parallelization strategies, helping readers develop an intuition for optimizing training and inference pipelines.While applicable across domains, the book places special emphasis on healthcare and biomedicine, where efficient AI can reduce costs and improve diagnostics, precision medicine, and clinical decision support. Real-world case studies and interviews with experts from organizations such as Google and Microsoft provide practical insights into building scalable healthcare AI systems. Aimed at graduate students, researchers, clinicians, biomedical engineers, data scientists, and AI practitioners, this book bridges algorithmic principles with applied implementation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
