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Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models, Ahmad Tafti, George Dimitoglou, Hamid Arabnia, Leonidas Deligiannidis


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Автор: Ahmad Tafti, George Dimitoglou, Hamid Arabnia, Leonidas Deligiannidis
Название:  Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models
ISBN: 9783111344003
Издательство: Walter de Gruyter
Классификация:


ISBN-10: 3111344002
Обложка/Формат: Hardback
Страницы: 500
Вес: 0.87 кг.
Дата издания: 15.07.2024
Серия: Intelligent computing
Язык: English
Иллюстрации: 200 illustrations, black and white
Размер: 178 x 246 x 29
Ключевые слова: Artificial intelligence,Computer networking & communications,Machine learning,Neural networks & fuzzy systems, COMPUTERS / Artificial Intelligence / General,COMPUTERS / Human-Computer Interaction (HCI),COMPUTERS / Machine Theory
Подзаголовок: Machine learning, convolutional neural networks and large language models
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Поставляется из: Германии


Accelerators for Convolutional Neural Networks

Автор: Arslan Munir, Joonho Kong, Mahmood Azhar Qureshi
Название: Accelerators for Convolutional Neural Networks
ISBN: 1394171889 ISBN-13(EAN): 9781394171880
Издательство: Wiley
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Цена: 17424.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Автор: Lu Le, Wang Xiaosong, Carneiro Gustavo
Название: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
ISBN: 3030139719 ISBN-13(EAN): 9783030139711
Издательство: Springer
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Цена: 10366.00 р.
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.

Convolutional Neural Networks for Medical Applications

Автор: Teoh
Название: Convolutional Neural Networks for Medical Applications
ISBN: 9811988137 ISBN-13(EAN): 9789811988134
Издательство: Springer
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Цена: 6097.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models.

Tree-Based Convolutional Neural Networks

Автор: Lili Mou; Zhi Jin
Название: Tree-Based Convolutional Neural Networks
ISBN: 9811318697 ISBN-13(EAN): 9789811318696
Издательство: Springer
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Цена: 6707.00 р.
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.

Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices

Автор: Abich
Название: Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices
ISBN: 3031185986 ISBN-13(EAN): 9783031185984
Издательство: Springer
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Цена: 9756.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book describes an extensive and consistent soft error assessment of convolutional neural network (CNN) models from different domains through more than 14.8 million fault injections, considering different precision bit-width configurations, optimization parameters, and processor models. The authors also evaluate the relative performance, memory utilization, and soft error reliability trade-offs analysis of different CNN models considering a compiler-based technique w.r.t. traditional redundancy approaches.

Advanced Applied Deep Learning

Автор: Umberto Michelucci
Название: Advanced Applied Deep Learning
ISBN: 1484249755 ISBN-13(EAN): 9781484249758
Издательство: Springer
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Цена: 5487.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.

What You Will LearnSee how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is ForScientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.
Guide to Convolutional Neural Networks

Автор: Hamed Habibi Aghdam; Elnaz Jahani Heravi
Название: Guide to Convolutional Neural Networks
ISBN: 3319861905 ISBN-13(EAN): 9783319861906
Издательство: Springer
Рейтинг:
Цена: 6097.00 р.
Наличие на складе: Нет в наличии.

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.

Iot-enabled convolutional neural networks: techniques and applications

Название: Iot-enabled convolutional neural networks: techniques and applications
ISBN: 877022725X ISBN-13(EAN): 9788770227254
Издательство: Taylor&Francis
Рейтинг:
Цена: 16078.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Guide to Convolutional Neural Networks for Computer Vision

Автор: Khan, Salman Rahmani, Hossein Shah, Syed Afaq Ali Bennamoun, Mohammed
Название: Guide to Convolutional Neural Networks for Computer Vision
ISBN: 3031006933 ISBN-13(EAN): 9783031006937
Издательство: Springer
Рейтинг:
Цена: 7927.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Practical Convolutional Neural Network Models

Автор: Pujari Pradeep, Sewak Mohit, Karim MD Rezaul
Название: Practical Convolutional Neural Network Models
ISBN: 1788392302 ISBN-13(EAN): 9781788392303
Издательство: Неизвестно
Рейтинг:
Цена: 7539.00 р.
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 ...

Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models

Автор: Ahmad Tafti, George Dimitoglou, Hamid Arabnia, Leonidas Deligiannidis
Название: Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models
ISBN: 3111344177 ISBN-13(EAN): 9783111344171
Издательство: Walter de Gruyter
Рейтинг:
Цена: 25848.00 р.
Наличие на складе: Нет в наличии.

Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

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: 19514.00 р.
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.


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