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Versandziele, Kosten & DauerAnbieter: GreatBookPrices, Columbia, MD, USA
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Anbieter: GreatBookPrices, Columbia, MD, USA
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Paperback. Zustand: new. Paperback. "Fine-Tuning LLMs: Advanced Techniques for Optimizing AI Performance with PyTorch and Hugging Face" is a definitive guide for practitioners, researchers, and developers who seek to harness the full potential of large language models in real-world applications. This comprehensive book demystifies the fine-tuning process, offering a step-by-step roadmap from foundational principles to state-of-the-art techniques.Inside, you'll discover how to leverage the powerful ecosystems of PyTorch and Hugging Face to tailor pre-trained models for specialized tasks across diverse industries-be it legal, medical, or financial. Learn how to prepare and preprocess data, implement parameter-efficient strategies, and optimize hyperparameters to ensure your models are both robust and scalable. In addition, the book covers essential topics such as ethical considerations, fairness, security, and responsible AI practices, equipping you with the tools needed to deploy safe and reliable systems.Packed with detailed case studies, real-world examples, and practical code snippets, this book not only teaches you the technical nuances of fine-tuning but also guides you through deployment, integration, and continuous learning in production environments. Whether you're a seasoned AI professional or an enthusiastic newcomer, "Fine-Tuning LLMs" provides the insights and methodologies necessary to transform cutting-edge research into impactful, high-performance applications.Embrace the future of AI with this essential resource that combines deep technical expertise with practical, hands-on guidance for optimizing language models to achieve unparalleled performance and efficiency. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798311657945
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Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: New. Bestandsnummer des Verkäufers 50033893-n
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Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
Zustand: As New. Unread book in perfect condition. Bestandsnummer des Verkäufers 50033893
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Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
Paperback. Zustand: new. Paperback. "Fine-Tuning LLMs: Advanced Techniques for Optimizing AI Performance with PyTorch and Hugging Face" is a definitive guide for practitioners, researchers, and developers who seek to harness the full potential of large language models in real-world applications. This comprehensive book demystifies the fine-tuning process, offering a step-by-step roadmap from foundational principles to state-of-the-art techniques.Inside, you'll discover how to leverage the powerful ecosystems of PyTorch and Hugging Face to tailor pre-trained models for specialized tasks across diverse industries-be it legal, medical, or financial. Learn how to prepare and preprocess data, implement parameter-efficient strategies, and optimize hyperparameters to ensure your models are both robust and scalable. In addition, the book covers essential topics such as ethical considerations, fairness, security, and responsible AI practices, equipping you with the tools needed to deploy safe and reliable systems.Packed with detailed case studies, real-world examples, and practical code snippets, this book not only teaches you the technical nuances of fine-tuning but also guides you through deployment, integration, and continuous learning in production environments. Whether you're a seasoned AI professional or an enthusiastic newcomer, "Fine-Tuning LLMs" provides the insights and methodologies necessary to transform cutting-edge research into impactful, high-performance applications.Embrace the future of AI with this essential resource that combines deep technical expertise with practical, hands-on guidance for optimizing language models to achieve unparalleled performance and efficiency. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798311657945
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