Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning Workloads
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Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning Workloads
Supercomputing for Artificial Intelligence is a practical, systems-oriented guide to mastering the infrastructures, tools, and execution trade-offs involved in scaling deep learning systems.
Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning Workloads
Artikelnr.: 211422612

Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning Workloads

Artikelnr.: 211422612

AWG 121

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Supercomputing for Artificial Intelligence is a practical, systems-oriented guide to mastering the infrastructures, tools, and execution trade-offs involved in scaling deep learning systems.
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Wat opvalt

Advanced Architectures
Explores cutting-edge architectures tailored for AI, enhancing computational efficiency and performance for deep learning applications, setting it apart from traditional computing resources.
Scalable Solutions
Provides strategies for scaling deep learning workloads, enabling researchers and developers to efficiently manage increasing data sizes and complexity, which is crucial for modern AI applications.
Comprehensive Foundations
Covers essential principles and frameworks of supercomputing for AI, equipping professionals with the foundational knowledge needed to innovate and excel in high-performance computing environments.

Productdetails

Shop Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning Workloads online at a best price in Aruba. B0F4YMMS7H
Publisher Independently published
Publication date July 30, 2025
Language English
Print length 804 pages
ISBN-13 979-8319328359
Item Weight 3.1 pounds (1.41 kg)
Dimensions 7.44 x 1.82 x 9.69 inches (18.9 x 4.6 x 24.6 cm)
Country of OriginThis item will be imported from US
Date First AvailableJanuary 13, 2026
What is in the boxSupercomputing for Artificial Intelligence:... For more details, please check description/product details

Voor wie is dit geschikt?

Suitable For
  • AI Researchers

    Ideal for researchers focusing on enhancing deep learning techniques, requiring extensive computational resources and advanced architectures.

  • Data Scientists

    Beneficial for data scientists looking to optimize their deep learning models using supercomputing strategies for large datasets.

  • Graduate Students

    Perfect for graduate students specializing in artificial intelligence, providing foundational knowledge of supercomputing in AI applications.

Not Suitable For
  • Casual Learners

    Not suitable for casual learners who want a simple introduction to AI without complex computational concepts.

PRODUCTBESCHRIJVING

Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning Workloads

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Vragen & Antwoorden van klanten

  • vraag: Do I need a supercomputer to use this book?

    antwoord: No, the book is designed to run on various systems, from single GPUs to HPC nodes.
  • vraag: Is this book suitable for beginners in AI?

    antwoord: Yes, it provides clear explanations and is accessible for learners at all levels.
  • vraag: Can I access the source code and examples online?

    antwoord: Yes, all source code and configuration files are available in a public GitHub repository.

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  • Comprehensive coverage of AI topics
  • Clear explanations and examples
  • Highly informative for professionals
  • Well-structured for easy understanding
  • Up-to-date with current trends

Nadelen

  • Some advanced sections may be challenging.

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Kenmerken en voordelen

  • Comprehensive guide to AI supercomputing and deep learning.
  • Focus on practical tools like PyTorch, CUDA, and MPI for scaling.
  • Covers performance optimization and distributed training techniques.
  • Field-tested content designed for students, researchers, and engineers.
  • Accessible to all levels, from single GPU setups to large HPC systems.
  • Includes source code and resources for hands-on learning.

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