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Item #:
28158151
Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
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Item #:
28158151
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We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail.
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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
- Written by PyTorch's creator and key contributors
- Develop deep learning models in a familiar Pythonic way
- Use PyTorch to build an image classifier for cancer detection
- Diagnose problems with your neural network and improve training with data augmentation
- Includes a free eBook in PDF, Kindle, and ePub formats
- Suitable for Python programmers with an interest in machine learning
| 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?
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Beginners in AI
Ideal for newcomers eager to understand neural networks and deep learning basics using practical examples.
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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
About This Item
Are you looking to dive into the world of deep learning and neural networks? Look no further than our product, "Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools." With PyTorch, a powerful and popular deep learning framework, you'll be able to harness the full potential of neural networks and unlock their capabilities. This first edition book provides a comprehensive guide on how to build, train, and optimize neural networks using the PyTorch library. Whether you're a beginner or an experienced data scientist, this book is designed to cater to your needs. The step-by-step tutorials and hands-on examples will help you grasp the fundamentals of PyTorch and its powerful features for neural network development. By leveraging Python tools, you'll have access to a wide range of libraries and resources that can enhance your deep learning projects.
From data manipulation to model building and deployment, this book covers it all. What sets PyTorch apart is its dynamic computational graph, which allows for flexibility and ease of use. You'll be able to experiment with different network architectures, fine-tune hyperparameters, and optimize your models efficiently. Whether you're working on AI applications, research projects, or industrial-scale deep learning implementations, PyTorch is a valuable tool in your arsenal. This book will guide you through the process of building deep learning models with PyTorch and equipping you with the knowledge to tackle real-world problems. Take your deep learning journey to new heights with "Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools." Start exploring the endless possibilities of PyTorch and unlock the full potential of neural networks today.
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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
Product Price History
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Features & Benefits
- Written by PyTorch's creator and key contributors
- Teaches to create deep learning and neural network systems with PyTorch
- Builds a tumor image classifier from scratch
- Covers best practices for the entire deep learning pipeline
- Includes downloadable Jupyter notebooks for all code samples
- For Python programmers with an interest in machine learning, no prior experience required
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