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
Numéro d'article: 81570099

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

Numéro d'article: 81570099

XOF 44199

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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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Ce qui se démarque

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.

Détails du produit

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 Senegal. 1803231335
Item Weight2 lbs (910 grams)

À qui est-ce destiné ?

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.

DESCRIPTION DU PRODUIT

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

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Questions et réponses des clients

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

    répondre: 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.
  • question: Who is the target audience for this book?

    répondre: 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.
  • question: What programming knowledge is required to make the most of this book?

    répondre: 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.
  • question: Does the book include practical exercises or projects?

    répondre: 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.
  • question: What are some key topics covered in this edition?

    répondre: 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.
  • question: How does this book differ from other computer vision resources?

    répondre: 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.
  • question: Can I use this book for self-study?

    répondre: 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.
  • question: Are there supplementary materials available for this book?

    répondre: 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.
  • question: Is prior knowledge of computer vision necessary to start with this book?

    répondre: 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.
  • question: 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'?

    répondre: 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 Senegal. 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.

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Caractéristiques et avantages

  • 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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