Introduction to PyTorch Practice ~ From the Basics of Deep Learning to Implementation Tankobon Softcover – January 30, 2021
The definitive implementation of deep learning by PyTorch!
Introduction to PyTorch Practice ~ From the Basics of Deep Learning to Implementation Tankobon Softcover – January 30, 2021
Numéro d'article: 88573717

Introduction to PyTorch Practice ~ From the Basics of Deep Learning to Implementation Tankobon Softcover – January 30, 2021

Numéro d'article: 88573717

XOF 25613

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Ce qui se démarque

Comprehensive Coverage
Offers a thorough introduction to deep learning concepts, from basic principles to advanced implementations, making it suitable for beginners and experienced practitioners alike.
Hands-On Practice
Includes practical exercises and projects that encourage readers to apply their knowledge, enhancing understanding and retention of PyTorch and deep learning techniques.
Accessible Language
Written in clear, approachable language, ensuring complex topics are easy to understand, which helps readers build confidence in their deep learning journey.

Détails du produit

Shop Introduction to PyTorch Practice ~ From the Basics of Deep Learning to Implementation Tankobon Softcover – January 30, 2021 online at a best price in Mali. 4839974691
Publisher u30deu30a4u30cau30d3u51fau7248 (January 30, 2021)
Publication date January 30, 2021
Language Japanese
Tankobon Softcover 608 pages
ISBN-10 4839974691
ISBN-13 978-4839974695
Dimensions 7.28 x 1.18 x 9.25 inches (18.5 x 3 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Beginner Data Scientists

    Perfect for those new to data science and looking to understand deep learning concepts using PyTorch.

  • Students in AI Courses

    Ideal for students enrolled in artificial intelligence courses seeking practical experience with deep learning frameworks.

  • Self-learners in Tech

    Great resource for individuals self-studying deep learning who want hands-on practice with real-world examples.

Not Suitable For
  • Advanced Developers

    Not suitable for experienced developers who already possess deep knowledge of PyTorch and deep learning techniques.

DESCRIPTION DU PRODUIT

Introduction to PyTorch Practice ~ From the Basics of Deep Learning to Implementation Tankobon Softcover – January 30, 2021

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What is the main focus of this book?

    répondre: The book focuses on teaching deep learning concepts and implementations using PyTorch.
  • question: Is prior knowledge of Python or machine learning required?

    répondre: While some familiarity with Python is helpful, the book provides a comprehensive explanation suitable for beginners.
  • question: What kind of projects does this book include?

    répondre: The book includes practical projects, such as an early detection model for lung cancer, along with deployment strategies.

Eli Stevens , Luca Antiga , Thomas Viehmann Editorial Review

The product, a Japanese-language book on deep learning with PyTorch, has received a mix of positive and critical feedback from customers. Many reviewers find it to be an excellent resource for beginners and recommend it for those just starting with deep learning or PyTorch. They praise the book for its clear explanations, well-structured content, and practical approach to teaching image object recognition, which is a central topic in the text. The inclusion of source code on Git adds significant value, allowing readers to verify their understanding as they progress through the material. However, some users highlight that while the book is beginner-friendly, it may become less practical for those looking to apply the concepts more broadly, mentioning that the book can feel overly focused on specific applications without providing enough context for broader understanding. Additionally, a few reviews indicate that the explanations of certain functions are insufficient and that some technical jargon may be overwhelming for readers without a strong programming background. The flow of some sections appears to be influenced by English structuring, making it slightly less readable for a Japanese audience. Overall, while the text is deemed essential for those serious about learning deep learning with PyTorch, it seems best suited for intermediate users who already have a foundational understanding of programming concepts. **

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Avantages

  • Clear, detailed explanations that are beginner-friendly.
  • Practical focus on image object recognition.
  • Availability of GitHub code for practical application.
  • Strong foundational emphasis on deep learning principles.

Les inconvénients

  • May be too focused on specific applications for broader learning.

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