Quantum Error Correction: Symmetric, Asymmetric, Synchronizable, and Convolutional Codes, La Guardia Giuliano Gadioli

Автор: Koonce, Brett
Название: Convolutional neural networks with swift for tensorflow
ISBN: 1484261674 ISBN-13(EAN): 9781484261675
Издательство: Springer
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Цена: 7685.00 р.
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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: Nora Webb Williams, Andreu Casas, John D. Wilkerso
Название: Images as Data for Social Science Research: An Introduction to Convolutional Neural Nets for Image Classification
ISBN: 1108816851 ISBN-13(EAN): 9781108816854
Издательство: Cambridge Academ
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Цена: 2851.00 р.
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Описание: Shows how innovation in computer vision methods can markedly lower the costs of using images as data. Introduces readers to deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. Provides guidance and instruction for scholars interested in using these methods in their own research.

Автор: La Guardia Giuliano Gadioli
Название: Quantum Error Correction: Symmetric, Asymmetric, Synchronizable, and Convolutional Codes
ISBN: 3030485501 ISBN-13(EAN): 9783030485504
Издательство: Springer
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Цена: 10480.00 р.
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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: 1461361680 ISBN-13(EAN): 9781461361688
Издательство: Springer
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Цена: 18284.00 р.
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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: Hamed Habibi Aghdam; Elnaz Jahani Heravi
Название: Guide to Convolutional Neural Networks
ISBN: 3319861905 ISBN-13(EAN): 9783319861906
Издательство: Springer
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Цена: 6986.00 р.
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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
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Цена: 7685.00 р.
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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.

Автор: Ajay Dholakia
Название: Introduction to Convolutional Codes with Applications
ISBN: 0792394674 ISBN-13(EAN): 9780792394679
Издательство: Springer
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Цена: 23508.00 р.
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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: Pujari Pradeep, Sewak Mohit, Karim MD Rezaul
Название: Practical Convolutional Neural Network Models
ISBN: 1788392302 ISBN-13(EAN): 9781788392303
Издательство: Неизвестно
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Цена: 7171.00 р.
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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: Zafar Iffat, Tzanidou Giounona, Burton Richard
Название: Hands-on Convolutional Neural Networks with Tensorflow
ISBN: 1789130336 ISBN-13(EAN): 9781789130331
Издательство: Неизвестно
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Цена: 6068.00 р.
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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: Ivan H. Dimovski
Название: Convolutional Calculus
ISBN: 9401067236 ISBN-13(EAN): 9789401067232
Издательство: Springer
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Цена: 13275.00 р.
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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: 22359.00 р.
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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: 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: 22359.00 р.
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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.