Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
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Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Masterfully introduces complex concepts in a way approachable for beginners and students.
Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Nº de artículo: 209714523

Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models

85% of respondents would recommend this to a friend

Nº de artículo: 209714523

USD 69

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What Stands Out

Comprehensive Coverage
This edition offers an in-depth exploration of deep learning concepts and techniques, catering to both beginners and advanced practitioners, enhancing their understanding of PyTorch effectively.
Hands-On Examples
Packed with practical exercises, this book provides readers with real-world applications of deep learning, enabling them to build and optimize models with confidence using PyTorch.
Updated Techniques
Reflecting the latest advancements in deep learning, the book covers cutting-edge practices, ensuring readers are well-versed in current industry trends and methodologies.

Detalles de producto

Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Publisher Manning Publications
Publication date March 10, 2026
Edition 2nd
Language English
Print length 544 pages
ISBN-10 1633438856
ISBN-13 978-1633438859
Item Weight 1.58 pounds (720 grams)
Dimensions 7.38 x 1.36 x 9.25 inches (18.7 x 3.5 x 23.5 cm)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners seeking to gain a solid foundational understanding of deep learning concepts and frameworks.

  • Machine Learning Enthusiasts

    Great for individuals interested in exploring advanced techniques and applications of machine learning using PyTorch.

  • Academic Researchers

    Useful for those involved in research requiring deep learning for experiments, particularly in artificial intelligence and related fields.

Not Suitable For
  • Absolute Beginners

    May not suit those without prior programming or machine learning experience, as concepts might be overly complex.

DESCRIPCIÓN DEL PRODUCTO

Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models

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Preguntas y respuestas de los clientes

  • Pregunta: What is the target audience for this book?

    Respuesta: The book is aimed at both beginners and advanced learners interested in deep learning and PyTorch.
  • Pregunta: Are there practical projects included?

    Respuesta: Yes, the book includes hands-on projects to reinforce concepts and apply them to real-world scenarios.
  • Pregunta: Can I access additional resources after purchase?

    Respuesta: Yes, you will receive a free eBook and access to the online liveBook format with an AI assistant.

Neural Networks Editorial Review

**** "Deep Learning with PyTorch, Second Edition" emerges as a pivotal resource for both newcomers and experienced practitioners delving into the realm of deep learning with PyTorch. Structured cohesively into two parts—Core PyTorch and Practical Applications—it caters effectively to diverse audiences by balancing theoretical foundations with real-world applications. Readers particularly appreciate the accessibility of Part 1, which begins with fundamental concepts like tensors and their interaction with NumPy arrays, making it a go-to for those with a Python or R background but new to PyTorch. The authors' decision to include practical, hands-on examples, such as using the HuggingFace transformers library, facilitates deeper understanding and engagement. The clarity of real-world examples, such as medical image analysis, significantly enhances the learning experience, showcasing not only how to build and evaluate models but also the complexities involved in challenging tasks, like detecting tumors in CT scans. The second part of the book dives into advanced topics, including scaling models for large datasets and deploying solutions. This edition impressively covers cutting-edge advancements in AI, such as large language models and attention mechanisms, ensuring it remains relevant in today’s rapidly evolving landscape. The progression of topics, from foundational deep learning to generative models, is notably well-paced, permitting readers to build confidence gradually. With each chapter accompanied by conclusions, exercises, and summaries, the structure is designed to reinforce learning and retention. Readers find themselves transitioning from a high-level understanding to a practical expertise in PyTorch, bridging the gap between theoretical learning and actual application. Moreover, the engaging writing style and comprehensible explanations make complex machine learning topics digestible. Customers laud the book as their indispensable reference manual for building and training deep learning models, highlighting it as an essential addition to any data scientist or AI engineer's library. In summary, this book stands out for its approachable presentation of complex material, the practical focus of its content, and its well-paced progression through deep learning themes. **

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ventajas

  • Clear and accessible explanations suited for both beginners and experienced practitioners.
  • Practical examples that enhance understanding and real-world applicability.
  • Updated content addressing recent advancements in AI and large language models.
  • Well-organized structure with conclusions, exercises, and summaries to reinforce learning.
  • Hands-on approach that bridges theory and practical application effectively.

Contras

  • Newcomers to deep learning may still find some topics challenging.

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USD 69

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características y beneficios

  • Covers all levels from beginner to advanced in deep learning with PyTorch.
  • Seamless structure enhances learning experience.
  • Practical projects help apply concepts to real-world problems.
  • Up-to-date content on the latest techniques in AI and ML.
  • Includes hands-on coding examples for building various neural networks.
  • Access to supplementary resources like eBooks and an AI assistant.

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