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Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond Paperback – 31 May 2024
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Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond Paperback – 31 May 2024
Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks.
Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond Paperback – 31 May 2024
商品＃: 104605672

Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond Paperback – 31 May 2024

商品＃: 104605672

TWD 2395

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What Stands Out

Comprehensive Coverage
Covers a wide range of deep learning techniques from CNNs to multimodal models, making it suitable for beginners and advanced users alike.
Practical Deployment
Focuses on real-world applications, providing insights on deploying models effectively, which is often overlooked in other resources.
Up-to-Date Content
Published in 2024, this book includes the latest advancements in PyTorch, ensuring readers stay current in the fast-evolving field of deep learning.

產品詳情

Shop Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond Paperback – 31 May 2024 online at a best price in Taiwan. 1801074305
Publisher Packt Publishing
Publication date 31 May 2024
Edition 2nd
Language English
Print length 558 pages
ISBN-10 1801074305
ISBN-13 978-1801074308
Item weight 939 g
Dimensions 19.05 x 3.18 x 23.5 cm
Country of OriginThis item will be imported from UK
Date First AvailableDecember 18, 2024
What is in the boxMastering PyTorch: Create and... For more details, please check description/product details

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners aiming to build foundational knowledge in deep learning and practical applications using PyTorch.

  • Machine Learning Engineers

    Great for professionals seeking to enhance their skills in creating and deploying advanced deep learning models.

  • Academic Researchers

    Useful for researchers interested in exploring cutting-edge techniques and multimodal approaches within their projects.

Not Suitable For
  • Complete Beginners

    Not suited for individuals with no prior programming or machine learning background, as it requires foundational knowledge.

產品描述

Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond Paperback – 31 May 2024

有疑問？ 和我們聊天

客戶問答

  • 問題: What is the primary focus of this book?

    Answer: The book focuses on mastering deep learning techniques using PyTorch, including model creation, training, and deployment.
  • 問題: Do I need prior experience with deep learning?

    Answer: Yes, a working knowledge of deep learning and Python is required to effectively use this book.
  • 問題: Will the book help me transition from TensorFlow to PyTorch?

    Answer: Absolutely, this book is ideal for those looking to switch from TensorFlow to PyTorch while implementing advanced models.

AI & Machine Learning Editorial Review

**** "Mastering PyTorch: Create and Deploy Deep Learning Models from CNNs to Multimodal Models, LLMs, and Beyond" is a comprehensive guide that effectively bridges the gap between practical coding and the theoretical underpinnings of deep learning with the PyTorch framework. Readers have praised the author, Ashish Ranjan Jha, for his mastery in conveying complex concepts in a clear and engaging manner, making this book a vital cornerstone for anyone looking to enhance their skills in artificial intelligence and machine learning. The book is particularly appreciated for its well-organized structure, where each chapter progressively builds upon the previous one. This allows readers, regardless of their prior knowledge, to develop a coherent understanding of diverse subjects, ranging from diffusion models to reinforcement learning and transformers. Moreover, the hands-on coding examples and real-world applications provide invaluable insights into how to optimize model training and deploy models effectively, catering to practitioners looking to work in real production environments. Advanced users have noted the necessity of an intermediate to advanced understanding of Python, as some complexity in the code might be challenging for newcomers. However, many have found the code provided to be fairly accessible and clear in logic. Suggestions for future editions include more detailed commentary on the code to further assist less experienced users. Additionally, readers appreciate the enriched coverage of the PyTorch ecosystem, including tools such as fastai and Hugging Face, with the author’s enthusiasm for these libraries inspiring users to explore new techniques and workflows. The fostering of a supportive community via a Discord channel further enhances the experience, as it enables peer collaboration and assistance. Overall, "Mastering PyTorch" stands out as an essential resource for anyone serious about deep learning, offering a robust mix of theoretical understanding and practical application. Professional reviewers and novice enthusiasts alike tout its effectiveness as a guide to navigating the complex world of AI and LLMs. **

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優點

  • Comprehensive coverage of both practical applications and theoretical aspects of deep learning.
  • Clear and engaging writing style, making complex topics accessible.
  • Well-organized structure that builds knowledge progressively.
  • In-depth examples of real-world applications and PyTorch ecosystem integration.
  • Useful community support through a Discord channel.

缺點

  • Requires an intermediate to advanced understanding of Python for complete comprehension.

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特色和優勢

  • Master advanced deep learning techniques using PyTorch.
  • Updated for PyTorch 2.x with cutting-edge integrations.
  • Build complex models from CNNs to multimodal applications.
  • Discover expert strategies for optimizing training and deployment.
  • Includes free eBook with purchase of the print or Kindle edition.
  • Ideal for data scientists and machine learning practitioners.

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