Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI
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Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI
The definitive book on computer vision is back and updated with the latest machine learning architecture, including 70+ pages on diffusion models
Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI
商品＃: 81570099

Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

商品＃: 81570099

JPY 10518

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The definitive book on computer vision is back and updated with the latest machine learning architecture, including 70+ pages on diffusion models
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この商品の利点

Comprehensive Framework
Covers the entire spectrum of computer vision, from essential theories to cutting-edge generative AI applications, making it suitable for learners at all levels.
Hands-On Approach
Utilizes practical, real-world examples and projects to enhance understanding, ensuring readers can apply learned concepts immediately in their work or research.
Updated Content
Includes the latest advancements in AI and deep learning, providing readers with current methodologies and trends, making the knowledge relevant and applicable in today's fast-evolving tech landscape.

製品詳細

Shop Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI online at a best price in 日本. 1803231335
Publisher Packt Publishing
Publication date June 10, 2024
Edition 2nd ed.
Language English
Print length 746 pages
ISBN-10 1803231335
ISBN-13 978-1803231334
Item Weight 2.77 pounds (1.26 kg)
Dimensions 7.5 x 1.68 x 9.25 inches (19.1 x 4.3 x 23.5 cm)

どんな人にお勧めですか？

Suitable For
  • Aspiring Data Scientists

    Ideal for those beginning their journey in data science and wanting to master computer vision using PyTorch.

  • Machine Learning Engineers

    Perfect for engineers seeking to improve their skills and implement advanced techniques in computer vision projects.

  • AI Researchers

    Beneficial for researchers looking to explore the latest advancements and generative AI applications in computer vision.

Not Suitable For
  • Total Beginners

    Not suitable for individuals with no prior programming or machine learning knowledge, as it assumes some familiarity.

製品説明書

Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

何か質問はありますか？ チャットでお問い合わせください

お客様からの質問と回答

  • 質問: What is 'Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI 2nd ed. Edition' about?

    回答: This book serves as a comprehensive guide for understanding modern computer vision concepts using PyTorch. It covers deep learning fundamentals and guides readers through advanced applications, including generative AI techniques. The structured approach makes it suitable for both beginners and experienced practitioners looking to enhance their knowledge in computer vision. Through practical examples and detailed explanations, readers can bridge the gap between theory and application in real-world projects.
  • 質問: Who is the target audience for this book?

    回答: The book is geared towards a diverse audience that includes students, researchers, and industry professionals interested in computer vision and deep learning. Beginners who are new to these concepts will find the introductory sections helpful, while experienced users can benefit from advanced techniques and applications covered in later chapters. This makes it ideal for anyone looking to expand their skill set in modern AI technologies.
  • 質問: What programming knowledge is required to make the most of this book?

    回答: A basic understanding of Python programming is essential for readers to effectively utilize the examples and exercises in the book. Familiarity with libraries such as NumPy and an introductory knowledge of machine learning concepts will enhance comprehension. This foundational knowledge will allow readers to engage with the practical projects throughout the book successfully.
  • 質問: Does the book include practical exercises or projects?

    回答: Yes, the book features numerous practical exercises and projects that encourage hands-on learning. These exercises allow readers to apply theoretical concepts to real-world scenarios, which not only reinforces learning but also builds invaluable skills. For example, readers will work on image classification tasks and delve into generative adversarial networks, thereby gaining experience in various computer vision applications.
  • 質問: What are some key topics covered in this edition?

    回答: Key topics in the 2nd edition include deep learning fundamentals, convolutional neural networks, image augmentation techniques, object detection, and the role of generative AI in computer vision. Each chapter builds upon the previous one, ensuring a coherent understanding of how these topics interrelate. This comprehensive coverage equips readers with versatile skills applicable in diverse fields such as robotics, healthcare, and autonomous vehicles.
  • 質問: How does this book differ from other computer vision resources?

    回答: Unlike many computer vision resources that focus heavily on theoretical aspects, this book emphasizes a practical approach. By using PyTorch, a popular deep learning framework, the authors provide hands-on examples that facilitate real-world application. Additionally, the integration of generative AI topics sets it apart, making it particularly relevant for those interested in cutting-edge developments in the field.
  • 質問: Can I use this book for self-study?

    回答: Absolutely! This book is designed to facilitate self-study, with clear explanations and step-by-step instructions that guide the reader through complex topics. Each chapter includes questions and projects that promote independent thinking and problem-solving skills. This self-paced approach allows readers to progress at their own speed while gaining relevant knowledge and skills in computer vision.
  • 質問: Are there supplementary materials available for this book?

    回答: Yes, supplementary materials, such as code examples, datasets, and additional resources, may be available through the publisher's website or other online repositories. These materials enhance the learning experience, making it easier for readers to experiment with various algorithms and models discussed in the book. Accessing these resources can significantly deepen understanding and facilitate practice.
  • 質問: Is prior knowledge of computer vision necessary to start with this book?

    回答: No prior knowledge of computer vision is necessary to begin with this book. It starts with fundamental concepts and progressively introduces complex topics. This approach ensures that even those with no background in the subject can grasp the essential ideas, making it an excellent starting point for beginners in the AI and machine learning domain.
  • 質問: Where can I buy 'Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI 2nd ed. Edition'?

    回答: You can buy 'Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI 2nd ed. Edition' on Ubuy in Japan. Ubuy is a reliable online platform that provides an easy shopping experience for this title, ensuring you can access this valuable resource for your learning needs.

Machine Theory Editorial Review

The "Modern Computer Vision with PyTorch - Second Edition" book has received highly positive feedback from customers. Reviewers appreciate the comprehensive coverage of modern AI techniques, including Detectron2, GANs, Deep Fakes, self-driving cars, Atari games, Multi-modal AI, Diffusion models, Model deployment, and vector stores. The practical examples and well-organized code snippets provided in the book have been particularly praised for their clarity and utility. Readers have found the explanations of complex concepts, such as transformers and diffusion models, to be easy to understand, helping them build a strong intuition for Generative AI. With a substantial length of over 700 pages and 18 chapters, the book serves as a bridge between academia and practical applications, catering to newbies and intermediate readers. It covers a range of topics, starting from neural networks and PyTorch basics, progressing to key computer vision concepts like CNNs, object detection, and segmentation. The book also delves into autoencoders, GANs, reinforcement learning, and interestingly, the integration of CV and NLP techniques. The section on vision transformers and OCR applications has been highlighted as intriguing. The final chapter on model deployment to production is described as highly useful, covering creating APIs, containerization, and cloud deployment. Overall, "Modern Computer Vision with PyTorch - Second Edition" is recommended for data scientists looking to stay updated with the latest trends in AI or individuals aiming to enter the field. **

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長所

  • Comprehensive coverage of modern AI techniques
  • Clear and well-organized code snippets
  • Easy-to-understand explanations of complex concepts
  • Practical examples for learning
  • Bridge between academia and practical applications

短所

  • No significant drawbacks mentioned

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JPY 10518

今すぐ注文すると 頃に届きます Sunday, 8月 16

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特徴と利点

  • Understand the inner workings of neural network architectures and their implementation, including transformers
  • Build solutions to real-world computer vision applications using PyTorch
  • Get to grips with CLIP and stable diffusion, and test their applications, such as in- and out-painting
  • Train a NN from scratch with NumPy and PyTorch
  • Implement 2D and 3D multi-object detection and segmentation
  • Learn about and implement diffusion models to harness the power of multimodal generative AI

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