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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2019-05-01 Publisher: Mechanical Industry Press Chapter 1 provides a quick review of the scientific principles of deep neural networks and the different frameworks for implementing such networks and the mathematical mechanisms behind them. Chapter 2 introduces the reader to convolutional neural networks and shows how to use deep learning to extract information from images. Chapter 3 builds on image classification problems from scratch.

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2020-07-01 Pages: 220 Publisher: Machinery Industry Press This book introduces the core of convolutional neural networks-the intricate details and the subtleties of the algorithm.?It mainly includes advanced topics of convolutional neural networks and object detection using Keras and TensorFlow.?Contents: Foreword Acknowledgements Chapter 1 Introduction and Setting up the Development Environment 1 1.1 GitHub Repository and Supporting Website 2 1.2 Essenti.

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 340 Publisher: Tsinghua University Press main content Understand the working mechanism of ANN and CNN Create computer vision applications and CNN using Python Using Tensorflow from the concept to production Using Num with Kivy .

paperback. Zustand: New. Paperback. Pub Date: 2021-10-01 Pages: 384 Publisher: Tsinghua University Press This book is based on the development of artificial intelligence as the background of the times. Through 20 machine learning models and algorithm cases. it provides readers with more detailed practical solutions for Deep learning.?In terms of layout. the book focuses on introducing the process of innovation projects. discussing data processing. model training and model application from the perspectives of overall .

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2018-07-01 Publisher: Mechanical Industry Press aims to comprehensively introduce the models. algorithms and applications of various convolutional neural networks. and guide readers to grasp the basic context of its formation and evolution to help readers in the shorter The time to get started from the level of mastery. Interested readers can start from the book. through the classification. identification. detection and segmentation of the image. graduall.

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2018-11-01 Pages: 200 Publisher: Electronic Industry Press Deep learning. especially deep convolutional neural networks is an important branch of artificial intelligence. and convolutional neural network technology is also widely used in various realities. The scene has achieved more than human intelligence on many issues. This book is an introductory book in the field. covering deep content.

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2021-08-01 Pages: 396 Publisher: Winner of the Turing Award of CITIC Publishing Group Co. Ltd. one of the Big Three of Deep Learning. Father of Convolutional Neural Networks. For his outstanding contributions in the field of intelligence. Yang Likun is well known by the Chinese computer science community and the business community.?Yang Likuns scientific journey composes a declaration of courage.?He studied for knowledge itself. not for a diploma. He .

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, an overview of DL is presented that adopts various perspectives such as state-of-the-arts deep learning techniques, Deep learning approaches, applications. Additionally, the potential problems on deep learning technology. This research presents convolutional neural networks (CNNs) which the most utilized DL network type. A survey of the CNN deep learning architectures that are frequently encountered in the literature, along with their strengths and limitations and describes the development of CNNs architectures together with their main features, e.g., AlexNet, VGG, ResNet, DenseNet, GoogLeNet, Inception: ResNet nd Inception V3/ V4, SegNet, U Net, Point CNN and MASK R-CNN .A detailed study on application of Convolutional Neural Network on the remote sensing to extract features is also explained. Challenges that met CNN were discussed.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many real-life machine learning applications are increasingly guiding into focus on object detection and recognition. The traditional computer vision approaches do not achieve the needed accuracies. Deep learning-based approaches have achieved high accuracy levels raising the interest in such approaches in recent years. License plate detection and recognition have been extensively studied over the decades. However, more accurate and national/language-independent approaches are still in the focus of today's demand. In this book, we discuss an approach to detect and recognize multinational and multilingual license plates. The approach has four modules and each module is implemented using convolutional neural network architecture. The YOLOv2 detector with ResNet core network is utilized for license plate detection module. Faster R-CNN detector with a custom core network architecture is used for character segmentation module. Low complexity convolutional neural network architectures for license plate classification and character recognition modules are analyzed and studied. Each module is trained and tested separately and used to build end-to-end license plate recognition system.