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83394025
Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
86% of respondents would recommend this to a friend
Item #:
83394025
AMD 27055
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What Stands Out
Hands-On Approach
Delivers practical techniques for implementing graph neural networks, enabling readers to gain real-world experience and apply their knowledge in actual projects effectively.
PyTorch Integration
Leveraging PyTorch's capabilities, it simplifies deep learning development while providing robust frameworks and tools tailored for graph-based modeling, making it accessible for developers at all levels.
Comprehensive Coverage
Covers a wide range of architectures and applications, equipping readers with a thorough understanding of graph neural networks to tackle diverse problems in various domains.
Product Details
| Publisher | Packt Publishing |
| Publication date | April 14, 2023 |
| Edition | 1st |
| Language | English |
| Print length | 354 pages |
| ISBN-10 | 1804617520 |
| ISBN-13 | 978-1804617526 |
| Item Weight | 1.43 pounds (650 grams) |
| Dimensions | 7.5 x 0.8 x 9.25 inches (19.1 x 2 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to leverage graph neural networks for advanced data analysis and modeling tasks.
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Machine Learning Developers
Great for developers who want to build deep learning applications using PyTorch, specifically focusing on graph structures.
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Students in AI
Perfect for students seeking practical guidance on graph neural networks and deep learning in an educational format.
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Beginners in Programming
Not suitable for absolute beginners who lack basic programming knowledge or experience with Python and machine learning.
Product Description
Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
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Intelligence & Semantics Editorial Review
The book "Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch" provides a good overview of the approaches and techniques used in graph neural networks. It also serves as a helpful introduction to the packages available for GNNs in Python. However, some weaknesses were identified in the reviews, such as the lack of detailed implementation explanations, glossing over key concepts, and not delving into why GNNs work better than other techniques in-depth. The book is noted for its clear explanations and functioning examples, making it suitable for individuals with basic ML/DL knowledge looking to delve into GNNs. While the physical book is of good quality, some readers found it cumbersome to use, suggesting opting for the free PDF version for a better reading experience. **
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Pros
- Great overview of GNN approaches and techniques
- Good introduction to Python packages for GNNs
- Clear explanations and functioning examples
Cons
- Lack of detailed implementation explanations
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