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Item #:
28158151
Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
88% of respondents would recommend this to a friend
Item #:
28158151
MAD 684
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What Stands Out
Hands-On Learning
Engages readers through practical projects, enabling them to implement and experiment with neural networks directly using PyTorch, reinforcing theoretical concepts with real-world applications.
Comprehensive Guidance
Provides detailed step-by-step instructions on building, training, and tuning models, making advanced deep learning techniques accessible even to beginners in Python and machine learning.
Up-to-Date Content
Covers the latest developments in deep learning and PyTorch, ensuring readers are equipped with current knowledge and skills to tackle modern AI challenges effectively.
Product Details
| Publisher | Manning |
| Publication date | August 4, 2020 |
| Edition | First Edition |
| Language | English |
| Print length | 520 pages |
| ISBN-10 | 1617295264 |
| ISBN-13 | 978-1617295263 |
| Item Weight | 1.95 pounds (880 grams) |
| Dimensions | 7.38 x 1.1 x 9.25 inches (18.7 x 2.8 x 23.5 cm) |
Who Should Buy?
-
Beginners in AI
Ideal for newcomers eager to understand neural networks and deep learning basics using practical examples.
-
Data Scientists
Data professionals looking to enhance their skills in developing and fine-tuning neural networks with PyTorch.
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Students
Perfect for students studying computer science or artificial intelligence, seeking a hands-on approach to deep learning.
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Advanced Experts
Experienced practitioners may find the content too basic and lacking advanced deep learning techniques.
Product Description
Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
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Intelligence & Semantics Editorial Review
"Deep Learning with PyTorch" provides a comprehensive introduction to the application of PyTorch in deep learning. The book covers topics such as PyTorch, tensors, backpropagation, convolutions, resnets, and more, making it a good starting point for those interested in learning about deep learning. However, the book is lacking a chapter on language models and attention. Additionally, the networks discussed in the book are mostly sequential, and the final example provided may not be very useful. The quality of the paper used for printing is poor, and the graphics are printed in black and white, which can make it difficult to read. Despite these shortcomings, the author explains the concepts carefully, with illustrations and graphs, making it easy to understand even for beginners.
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Pros
- Comprehensive introduction to PyTorch and deep learning
- Thorough explanation of concepts with illustrations and graphs
- Provides good starting point for beginners
Cons
- Lacking a chapter on language models and attention
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