deep learning convolutional neural (93 risultati)

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Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. Convolutional neural networks have emerged as a powerful paradigm for deep learning, enabling models to learn from image and signal data. In 'Deep Learning for Convolutional Neural Networks', we provide a comprehensive overview of the current state of convolutional neural networks, including their theoretical foundations, practical applications, and recent advances. This book explores the benefits of convolutional neural networks, such as improved feature learning and object recognition, and presents a range of techniques for implementing convolutional neural networks in deep learning systems. With a focus on real-world examples and case studies, this book offers a thorough guide to convolutional neural networks, making it an invaluable resource for researchers and practitioners in the field of deep learning. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. Object Detection with Convolutional Neural Networks and Deep Learning Techniques presents a comprehensive exploration of the application of convolutional neural networks and deep learning techniques in object detection. By examining the latest techniques and algorithms in object detection, this book provides a thorough understanding of how to develop efficient and effective object detection systems. From the fundamentals of convolutional neural networks to the implementation of deep learning-based object detection pipelines, Object Detection with Convolutional Neural Networks and Deep Learning Techniques offers a valuable resource for anyone seeking to develop object detection systems for a wide range of applications. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condizione: New. Condizione sovraccoperta: no dj. First. Object Detection with Convolutional Neural Networks and Deep Learning is a cutting-edge resource for researchers and practitioners seeking to master the art of object detection using state-of-the-art deep learning techniques. This book provides a thorough introduction to the fundamental concepts and architectures of convolutional neural networks, including their application to object detection tasks. Through a series of in-depth case studies and experiments, the author demonstrates the efficacy of deep learning methods for object detection in various domains, including computer vision and robotics. By exploring the latest advancements in this field, readers can develop the skills and knowledge necessary to tackle complex object detection challenges. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

paperback. Condizione: 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. Condizione: 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. Condizione: 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. Condizione: 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. Condizione: 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. Condizione: 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. Condizione: 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. Condizione: 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. Condizione: 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.