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Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail.
Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
artikal br.: 28158151

Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools

artikal br.: 28158151

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RSD 7787

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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.

Детаљи о производу

Learn how to build, train, and tune neural networks using Python tools. Discover the power of deep learning with PyTorch. Get the first edition now! Shop at Ubuy Serbia
  • 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?

Suitable For
  • 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.

  • Students

    Perfect for students studying computer science or artificial intelligence, seeking a hands-on approach to deep learning.

Not Suitable For
  • Advanced Experts

    Experienced practitioners may find the content too basic and lacking advanced deep learning techniques.

OPIS PROIZVODA

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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  • Pitanje: Како купити Deep Learning with PyTorch: Build, train, and tune на мрежи од Убуи-а?

    Odgovor: Лако је куповати Deep Learning with PyTorch: Build, train, and tune на мрежи од Убуи-а.. Морате само да потражите производ, изаберете начин испоруке приликом одјављивања и да га испоручите на вашу локацију.
  • Pitanje: Да ли је Deep Learning with PyTorch: Build, train, and tune доступан за онлајн куповину у Serbia?

    Odgovor: Да, у Убуи Serbia овај производ је доступан за куповину по разумној цени.. Deep Learning with PyTorch: Build, train, and tune није доступан локално, али можете нам веровати у наше услуге експресне испоруке.
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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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Прос

  • Comprehensive introduction to PyTorch and deep learning
  • Thorough explanation of concepts with illustrations and graphs
  • Provides good starting point for beginners

Цонс

  • Lacking a chapter on language models and attention

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