Item #:
88573717
Introduction to PyTorch Practice ~ From the Basics of Deep Learning to Implementation Tankobon Softcover – January 30, 2021
92% of respondents would recommend this to a friend
Item #:
88573717
XAF 24305
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What Stands Out
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.
Product Details
| 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) |
Who Should Buy?
-
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.
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Advanced Developers
Not suitable for experienced developers who already possess deep knowledge of PyTorch and deep learning techniques.
Product Description
Introduction to PyTorch Practice ~ From the Basics of Deep Learning to Implementation Tankobon Softcover – January 30, 2021
Have any Query? Chat with us
Customer Questions & Answers
-
Question:
What is the main focus of this book?
Answer: The book focuses on teaching deep learning concepts and implementations using PyTorch. -
Question:
Is prior knowledge of Python or machine learning required?
Answer: 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?
Answer: 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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Pros
- 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.
Cons
- May be too focused on specific applications for broader learning.
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Features & Benefits
- Comprehensive guide to deep learning concepts and PyTorch.
- Practical project-based learning with real data.
- Best practices for training neural networks with limited data.
- Step-by-step implementation instructions for model training.
- Various methods for deploying models to production environments.
- Ideal for developers looking to enhance their deep learning skills.
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