Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
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Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Masterfully introduces complex concepts in a way approachable for beginners and students.
Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
Nº de artículo: 209714523

Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models

86% of respondents would recommend this to a friend

Nº de artículo: 209714523

HNL 1804

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What Stands Out

Comprehensive Coverage
This edition offers an in-depth exploration of deep learning concepts and techniques, catering to both beginners and advanced practitioners, enhancing their understanding of PyTorch effectively.
Hands-On Examples
Packed with practical exercises, this book provides readers with real-world applications of deep learning, enabling them to build and optimize models with confidence using PyTorch.
Updated Techniques
Reflecting the latest advancements in deep learning, the book covers cutting-edge practices, ensuring readers are well-versed in current industry trends and methodologies.

Detalles de producto

Shop Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models online at a best price in Honduras. 1633438856
  • Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.PyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models.Instantly familiar to anyone who knows PyData tools like NumPy, PyTorch simplifies deep learning without sacrificing advanced features. In this book you’ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch’s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. You’ll discover how easy PyTorch makes it to build your entire DL pipeline, including using the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action with practical code examples in each chapter, culminating into you building your own convolution neural networks, transformers, and even a real-world medical image classifier.In Deep Learning with PyTorch, Second Edition you’ll find:• Deep learning fundamentals reinforced with hands-on projects• Mastering PyTorch's flexible APIs for neural network development• Implementing CNNs, transformers, and diffusion models• Optimizing models for training and deployment• Generative AI models to create images and textAbout the technologyThe powerful PyTorch library makes deep learning simple—without sacrificing the features you need to create efficient neural networks, LLMs, and other ML models. Pythonic by design, it’s instantly familiar to users of NumPy, Scikit-learn, and other ML frameworks. This thoroughly-revised second edition covers the latest PyTorch innovations, including how to create and refine generative AI models.About the bookDeep Learning with PyTorch, Second Edition shows you how to build neural network models using the latest version of PyTorch. Clear explanations and practical projects help you master the fundamentals and explore advanced architectures including transformers and LLMs. Along the way you’ll learn techniques for training using augmented data, improving model architecture, and fine tuning.What's inside• PyTorch APIs for neural network development• LLMs, transformers, and diffusion models• Model training and deploymentAbout the readerFor Python programmers with a background in machine learning.About the authorHoward Huang is a software engineer and developer on the PyTorch library focusing on large scale, distributed training. Eli Stevens, Luca Antiga, and Thomas Viehmann authored the first edition of Deep Learning with PyTorch.Table of ContentsPart 11 Introducing deep learning and the PyTorch library2 Pretrained networks3 It starts with a tensor4 Real-world data representation using tensors5 The mechanics of learning6 Using a neural network to fit the data7 Telling birds from airplanes: Learning from images8 Using convolutions to generalizePart 29 How transformers work10 Diffusion models for images11 Using PyTorch to fight cancer12 Combining data sources into a unified dataset13 Training a classification model to detect suspected tumors14 Improving training with metrics and augmentation15 Using segmentation to find suspected nodules16 Training models on multiple GPU17 Deploying to production
Publisher Manning Publications
Publication date March 10, 2026
Edition 2nd
Language English
Print length 544 pages
ISBN-10 1633438856
ISBN-13 978-1633438859
Item Weight 1.58 pounds (720 grams)
Dimensions 7.38 x 1.36 x 9.25 inches (18.7 x 3.5 x 23.5 cm)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners seeking to gain a solid foundational understanding of deep learning concepts and frameworks.

  • Machine Learning Enthusiasts

    Great for individuals interested in exploring advanced techniques and applications of machine learning using PyTorch.

  • Academic Researchers

    Useful for those involved in research requiring deep learning for experiments, particularly in artificial intelligence and related fields.

Not Suitable For
  • Absolute Beginners

    May not suit those without prior programming or machine learning experience, as concepts might be overly complex.

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Preguntas y respuestas de los clientes

  • Pregunta: What is Deep Learning with PyTorch, Second Edition about?

    Respuesta: Deep Learning with PyTorch, Second Edition, offers comprehensive insights into deep learning concepts and practical applications using PyTorch. The book covers fundamental theories, cutting-edge techniques, and hands-on projects tailored for creating Generative AI models. Readers will gain a solid foundation in neural networks, convolutional networks, and reinforcement learning. This edition also integrates modern advancements, making it perfect for both beginners and experienced practitioners looking to enhance their skills and knowledge.
  • Pregunta: Who should read Deep Learning with PyTorch, Second Edition?

    Respuesta: This book is ideal for data scientists, machine learning practitioners, and students who wish to deepen their understanding of deep learning. It is particularly valuable for those interested in applying PyTorch for real-world projects. Professionals transitioning from traditional programming to AI development will also find it beneficial. Whether you're a newcomer or have some experience, this guide provides essential insights and practical examples to foster your learning journey.
  • Pregunta: What kind of projects can I expect in this book?

    Respuesta: The book features various hands-on projects, including image classification, natural language processing, and the development of generative models. Each project is structured to facilitate learning, guiding readers through the implementation process step-by-step. These practical applications help solidify theoretical knowledge while encouraging creativity in applying deep learning techniques. Ideal for boosting your portfolio, these projects can serve as great references for future work or academic pursuits.
  • Pregunta: Does the book cover advancements in generative AI?

    Respuesta: Yes, the second edition delves into generative AI, focusing on building models that can create new content. This includes techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The inclusion of these topics highlights the book's commitment to staying current with the latest trends and tools in AI development. Readers will learn how to implement these advanced models for various applications, enhancing their ability to work in innovative areas of technology.
  • Pregunta: Is prior knowledge of Python required?

    Respuesta: While prior experience with Python is beneficial, it is not strictly required. The book provides foundational programming concepts intertwined with deep learning principles, making it accessible for those willing to learn. Beginners may need to invest some time in familiarizing themselves with Python basics, especially the libraries used in AI such as NumPy and Pandas. Overall, enthusiastic learners with a motivation to explore deep learning can successfully navigate this guide regardless of their initial skill level.
  • Pregunta: How does this book compare to other deep learning resources?

    Respuesta: Deep Learning with PyTorch, Second Edition stands out due to its practical approach and the use of PyTorch, a popular framework in the industry. Unlike many theoretical texts, this book emphasizes hands-on experience, allowing readers to apply what they learn immediately. Additionally, its updated coverage of contemporary AI techniques ensures that readers engage with relevant content. This makes it a go-to resource not only for academics but also for professionals looking to enhance their practical skills in real-world applications.
  • Pregunta: Can this book help me prepare for a career in AI?

    Respuesta: Absolutely! This book equips readers with the knowledge and skills needed to excel in AI careers by providing practical examples and comprehensive explanations of complex concepts. By working through the projects and techniques outlined in the book, readers can build a portfolio that demonstrates their competence in deep learning and PyTorch, making them attractive candidates to potential employers. Additionally, the hands-on nature fosters problem-solving abilities which are crucial in the fast-evolving tech landscape.
  • Pregunta: What format is the book available in?

    Respuesta: Deep Learning with PyTorch, Second Edition is typically available in both print and digital formats. The digital version allows for portability and ease of access, enabling you to study on-the-go, while the print version provides a tangible resource for referencing techniques. Both formats are designed to enhance the learning experience by providing clear visuals and structured content. This flexibility ensures that readers can choose an option that best suits their learning preferences.
  • Pregunta: Are there online resources or communities for readers?

    Respuesta: Yes, many online communities, forums, and resources support readers of Deep Learning with PyTorch. Websites like GitHub, Stack Overflow, and specialized forums provide platforms for discussion, troubleshooting, and collaboration. Readers can join these communities to share insights and build connections with other learners and professionals. Additionally, many online courses and video tutorials complement the book, offering varied learning tools to reinforce the material covered. Participating in these communities is a great way to enhance one’s understanding and network with like-minded individuals.
  • Pregunta: Where can I buy Deep Learning With PyTorch, Second Edition in Honduras?

    Respuesta: You can buy Deep Learning With PyTorch, Second Edition on Ubuy, which offers a wide selection of books and educational materials. Ubuy is a reliable platform catering to various customer needs, providing easy access to this essential resource for mastering deep learning principles. It’s an excellent choice for obtaining the book swiftly and securely while benefiting from various shipping options tailored for your convenience.

Neural Networks Editorial Review

**** "Deep Learning with PyTorch, Second Edition" emerges as a pivotal resource for both newcomers and experienced practitioners delving into the realm of deep learning with PyTorch. Structured cohesively into two parts—Core PyTorch and Practical Applications—it caters effectively to diverse audiences by balancing theoretical foundations with real-world applications. Readers particularly appreciate the accessibility of Part 1, which begins with fundamental concepts like tensors and their interaction with NumPy arrays, making it a go-to for those with a Python or R background but new to PyTorch. The authors' decision to include practical, hands-on examples, such as using the HuggingFace transformers library, facilitates deeper understanding and engagement. The clarity of real-world examples, such as medical image analysis, significantly enhances the learning experience, showcasing not only how to build and evaluate models but also the complexities involved in challenging tasks, like detecting tumors in CT scans. The second part of the book dives into advanced topics, including scaling models for large datasets and deploying solutions. This edition impressively covers cutting-edge advancements in AI, such as large language models and attention mechanisms, ensuring it remains relevant in today’s rapidly evolving landscape. The progression of topics, from foundational deep learning to generative models, is notably well-paced, permitting readers to build confidence gradually. With each chapter accompanied by conclusions, exercises, and summaries, the structure is designed to reinforce learning and retention. Readers find themselves transitioning from a high-level understanding to a practical expertise in PyTorch, bridging the gap between theoretical learning and actual application. Moreover, the engaging writing style and comprehensible explanations make complex machine learning topics digestible. Customers laud the book as their indispensable reference manual for building and training deep learning models, highlighting it as an essential addition to any data scientist or AI engineer's library. In summary, this book stands out for its approachable presentation of complex material, the practical focus of its content, and its well-paced progression through deep learning themes. **

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ventajas

  • Clear and accessible explanations suited for both beginners and experienced practitioners.
  • Practical examples that enhance understanding and real-world applicability.
  • Updated content addressing recent advancements in AI and large language models.
  • Well-organized structure with conclusions, exercises, and summaries to reinforce learning.
  • Hands-on approach that bridges theory and practical application effectively.

Contras

  • Newcomers to deep learning may still find some topics challenging.

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características y beneficios

  • Covers all levels from beginner to advanced in deep learning with PyTorch.
  • Seamless structure enhances learning experience.
  • Practical projects help apply concepts to real-world problems.
  • Up-to-date content on the latest techniques in AI and ML.
  • Includes hands-on coding examples for building various neural networks.
  • Access to supplementary resources like eBooks and an AI assistant.

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