Quantum Error Correction: Symmetric, Asymmetric, Synchronizable, and Convolutional Codes, La Guardia Giuliano Gadioli
Автор: Zafar Iffat, Tzanidou Giounona, Burton Richard Название: Hands-on Convolutional Neural Networks with Tensorflow ISBN: 1789130336 ISBN-13(EAN): 9781789130331 Издательство: Неизвестно Рейтинг: Цена: 40450.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!
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 Рейтинг: 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: La Guardia Giuliano Gadioli Название: Quantum Error Correction: Symmetric, Asymmetric, Synchronizable, and Convolutional Codes ISBN: 3030485501 ISBN-13(EAN): 9783030485504 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This text presents an algebraic approach to the construction of several important families of quantum codes derived from classical codes by applying the well-known Calderbank-Shor-Steane (CSS), Hermitian, and Steane enlargement constructions to certain classes of classical codes.
Автор: Ajay Dholakia Название: Introduction to Convolutional Codes with Applications ISBN: 0792394674 ISBN-13(EAN): 9780792394679 Издательство: Springer Рейтинг: Цена: 156720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: An introduction to the basic concepts of convolutional codes, their structure and classification, various error correction and decoding techniques for convolutionally encoded data, and some of the most common applications. This book discusses the definition and representations, distance properties, and important classes of convolutional codes.
Автор: Ajay Dholakia Название: Introduction to Convolutional Codes with Applications ISBN: 1461361680 ISBN-13(EAN): 9781461361688 Издательство: Springer Рейтинг: Цена: 121890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Introduction to Convolutional Codes with Applications is an introduction to the basic concepts of convolutional codes, their structure and classification, various error correction and decoding techniques for convolutionally encoded data, and some of the most common applications.
Автор: Koonce, Brett Название: Convolutional neural networks with swift for tensorflow ISBN: 1484261674 ISBN-13(EAN): 9781484261675 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: MNIST: 1D Neural Network Chapter 2: MNIST: 2D Neural Network Chapter 3: CIFAR: 2D Nueral Network with Blocks Chapter 4: VGG Network Chapter 5: Resnet 34 Chapter 6: Resnet 50 Chapter 7: SqueezeNet Chapter 8: MobileNrt v1 Chapter 9: MobileNet v2 Chapter 10: Evolutionary Strategies Chapter 11: MobileNet v3 Chapter 12: Bag of Tricks Chapter 13: MNIST Revisited Chapter 14: You are Here
Автор: Pujari Pradeep, Sewak Mohit, Karim MD Rezaul Название: Practical Convolutional Neural Network Models ISBN: 1788392302 ISBN-13(EAN): 9781788392303 Издательство: Неизвестно Рейтинг: Цена: 47810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN`s best practices to implement smart ConvNet ...
Автор: Ivan H. Dimovski Название: Convolutional Calculus ISBN: 9401067236 ISBN-13(EAN): 9789401067232 Издательство: Springer Рейтинг: Цена: 88500.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Le Lu Название: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics ISBN: 3030139689 ISBN-13(EAN): 9783030139681 Издательство: Springer Рейтинг: 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: Hamed Habibi Aghdam; Elnaz Jahani Heravi Название: Guide to Convolutional Neural Networks ISBN: 3319861905 ISBN-13(EAN): 9783319861906 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Поставка под заказ. Описание: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification.
Автор: Lili Mou; Zhi Jin Название: Tree-Based Convolutional Neural Networks ISBN: 9811318697 ISBN-13(EAN): 9789811318696 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data. TBCNNsare related to existing convolutional neural networks (CNNs) and recursive neural networks (RNNs), but they combine the merits of both: thanks to their short propagation path, they are as efficient in learning as CNNs; yet they are also as structure-sensitive as RNNs. In this book, readers will also find a comprehensive literature review of related work, detailed descriptions of TBCNNs and their variants, and experiments applied to program analysis and natural language processing tasks. It is also an enjoyable read for all those with a general interest in deep learning.
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