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Building DeepSeek AI Models: Architecture, Implementation, and Optimization: 7 (Advanced Topics in Machine Learning) - Softcover
Inhaltsangabe
This book offers an in-depth exploration of the design, implementation, and optimization of DeepSeek AI models, blending theoretical rigor with advanced engineering insights. It unravels the complexities of cutting-edge deep learning techniques—including transformer architectures, Mixture-of-Experts, and reinforcement learning fine-tuning—equipping researchers and engineers with the expertise to build, scale, and deploy large language models with precision and efficiency.
With a strong focus on algorithmic advancements and hardware optimizations, this guide addresses the pressing challenges of training ultra-large models, ensuring efficiency, scalability, and reliability. Rich with practical blueprints and real-world case studies, it showcases applications from code intelligence to multi-step reasoning, offering a comprehensive roadmap for AI practitioners.
By integrating discussions on data preprocessing, distributed training, and custom GPU optimization libraries, this book serves as an indispensable resource for those pushing the boundaries of open-source AI research—fostering innovation, collaboration, and the future of large-scale deep learning.
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- VerlagIndependently published
- Erscheinungsdatum2025
- ISBN 13 9798313438481
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten223
- Kontakt zum HerstellerNicht verfügbar
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Building DeepSeek AI Models: Architecture, Implementation, and Optimization (Advanced Topics in Machine Learning)
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Building DeepSeek AI Models: Architecture, Implementation, and Optimization (Advanced Topics in Machine Learning)
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Building DeepSeek AI Models (Paperback)
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Paperback. Zustand: new. Paperback. This book offers an in-depth exploration of the design, implementation, and optimization of DeepSeek AI models, blending theoretical rigor with advanced engineering insights. It unravels the complexities of cutting-edge deep learning techniques-including transformer architectures, Mixture-of-Experts, and reinforcement learning fine-tuning-equipping researchers and engineers with the expertise to build, scale, and deploy large language models with precision and efficiency. With a strong focus on algorithmic advancements and hardware optimizations, this guide addresses the pressing challenges of training ultra-large models, ensuring efficiency, scalability, and reliability. Rich with practical blueprints and real-world case studies, it showcases applications from code intelligence to multi-step reasoning, offering a comprehensive roadmap for AI practitioners. By integrating discussions on data preprocessing, distributed training, and custom GPU optimization libraries, this book serves as an indispensable resource for those pushing the boundaries of open-source AI research-fostering innovation, collaboration, and the future of large-scale deep learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798313438481
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Building DeepSeek AI Models (Paperback)
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Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. This book offers an in-depth exploration of the design, implementation, and optimization of DeepSeek AI models, blending theoretical rigor with advanced engineering insights. It unravels the complexities of cutting-edge deep learning techniques-including transformer architectures, Mixture-of-Experts, and reinforcement learning fine-tuning-equipping researchers and engineers with the expertise to build, scale, and deploy large language models with precision and efficiency. With a strong focus on algorithmic advancements and hardware optimizations, this guide addresses the pressing challenges of training ultra-large models, ensuring efficiency, scalability, and reliability. Rich with practical blueprints and real-world case studies, it showcases applications from code intelligence to multi-step reasoning, offering a comprehensive roadmap for AI practitioners. By integrating discussions on data preprocessing, distributed training, and custom GPU optimization libraries, this book serves as an indispensable resource for those pushing the boundaries of open-source AI research-fostering innovation, collaboration, and the future of large-scale deep learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798313438481
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