Fundamentals of Cost-Efficient AI: In Healthcare and Biomedicine is situated within the broader context of artificial intelligence (AI) and deep learning (DL) technologies as they pertain to healthcare and biomedicine. AI and DL have witnessed remarkable advancements in recent years, leading to breakthroughs in various applications, including medical image analysis, drug discovery, disease diagnosis, and personalized treatment recommendations. In 14 chapters this book offers techniques and best practices for compressing deep learning models, allowing them to run efficiently on healthcare devices with limited memory and processing power. This book provides the void code-first approach to optimizing AI for healthcare and biomedicine by offering practical solutions to the unique challenges posed by AI in healthcare. The growing demand for efficient AI computing techniques, coupled with advancements in Deep Learning and Large Language Models tailored for healthcare and biomedicine, makes this book, Efficient Cost Aware Artificial Intelligence in Healthcare and Biomedicine timely and essential. It addresses the pressing need for practical guidance on compressing and optimizing AI models for everyday healthcare devices, making it accessible to a broad and diverse audience seeking to harness the power of AI within the healthcare and biomedicine sectors. "Efficient AI in Healthcare and Biomedicine" empowers healthcare professionals, researchers, and organizations with practical solutions to the challenges posed by resource-intensive deep learning models in healthcare and biomedicine.
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Rohit Kumar is a highly accomplished executive with many years of experience in the US Silicon Valley tech industry, specializing in innovative applications of AI and machine learning within the domains of healthcare and biomedicine. As a first-generation serial entrepreneur and investor in multiple successful startups, Rohit's contributions have left an indelible mark on the intersection of technology and healthcare.
Currently, Rohit serves as the CTO of SublimeAI, a US-based AI company that has made significant strides in revolutionizing healthcare through AI-driven solutions. In this role, he spearheads the development of cutting-edge AI technologies aimed at improving patient care, diagnostics, and medical research.
Additionally, Rohit heads R&D for CSC - an initiative of the Ministry of Electronics and IT (MeitY), Government of India, with a primary focus on harnessing AI to address critical healthcare challenges in the Indian contextLes informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Paperback. Etat : 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. N° de réf. du vendeur 9780443333620
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