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A Guide to Convolutional Neural Networks for Computer Vision

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Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks o Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.


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Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks o Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

30 review for A Guide to Convolutional Neural Networks for Computer Vision

  1. 4 out of 5

    Renee

    A very thorough and efficient book about neural networks and decent applications to use when trying to build one. I would've enjoyed a bit more from a security standpoint on attack methods and how to fight them, it was just a tiny blurb at the beginning and end, but definitely an effective learning tool. A very thorough and efficient book about neural networks and decent applications to use when trying to build one. I would've enjoyed a bit more from a security standpoint on attack methods and how to fight them, it was just a tiny blurb at the beginning and end, but definitely an effective learning tool.

  2. 5 out of 5

    Yanwei Liu

    I like the Sixth chapter: "Examples of CNN Architectures", it clearly introduces the concept of the popular architectures like LeNet, AlexNet, VGGNet, ResNet ....... I recommend this book for anyone who wants to understand the classic CNN architectures. I like the Sixth chapter: "Examples of CNN Architectures", it clearly introduces the concept of the popular architectures like LeNet, AlexNet, VGGNet, ResNet ....... I recommend this book for anyone who wants to understand the classic CNN architectures.

  3. 5 out of 5

    Eberli

  4. 4 out of 5

    Olivia

  5. 5 out of 5

    Trevor

  6. 4 out of 5

    Madrid

  7. 5 out of 5

    Israel

  8. 4 out of 5

    Bill Peckham

  9. 5 out of 5

    Thanh Hương

  10. 5 out of 5

    محمد

  11. 4 out of 5

    Hannah Wilcox

  12. 5 out of 5

    Chukson

  13. 4 out of 5

    Alexander

  14. 5 out of 5

    YOON, JONGHYUN

  15. 4 out of 5

    Michael Sprayberry

  16. 5 out of 5

    nat

  17. 4 out of 5

    Arto Bendiken

  18. 5 out of 5

    Sebastian

  19. 4 out of 5

    Martin

  20. 4 out of 5

    Eric

  21. 4 out of 5

    Nguyet Dang

  22. 4 out of 5

    Muhammed Vawda

  23. 5 out of 5

    Melissa Slater

  24. 5 out of 5

    hello world

  25. 5 out of 5

    Reet

  26. 4 out of 5

    Rosc Piko

  27. 4 out of 5

    Cao

  28. 5 out of 5

    Airz Watari

  29. 5 out of 5

    Henry Cooksley

  30. 5 out of 5

    Glen Ritschel

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