Tree-Based Convolutional Neural Networks, Lili Mou; Zhi Jin

Автор: Venkatesan, Ragav,
Название: Convolutional Neural Networks In Vi
ISBN: 1498770398 ISBN-13(EAN): 9781498770392
Издательство: Taylor&Francis
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Цена: 168430.00 T
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Описание: This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner`s guide to engineers or students who want to have a quick start on learning and/or building deep learning systems.

Автор: Le Lu; Yefeng Zheng; Gustavo Carneiro; Lin Yang
Название: Deep Learning and Convolutional Neural Networks for Medical Image Computing
ISBN: 3319827138 ISBN-13(EAN): 9783319827131
Издательство: Springer
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Цена: 149060.00 T
Наличие на складе: Поставка под заказ.
Описание: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Автор: Le Lu
Название: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
ISBN: 3030139689 ISBN-13(EAN): 9783030139681
Издательство: Springer
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Цена: 149060.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases.

Автор: Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun
Название: A Guide to Convolutional Neural Networks for Computer Vision
ISBN: 1681732785 ISBN-13(EAN): 9781681732787
Издательство: Mare Nostrum (Eurospan)
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Цена: 102570.00 T
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Описание: 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.