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Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch (Paperback or Softback)
Language: English
Published by Packt Publishing 4/14/2023, 2023
ISBN 10: 1804617520 ISBN 13: 9781804617526
Paperback or Softback. Condition: New. Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch. Book.
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Condition: As New. Unread book in perfect condition.
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Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 43.32
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Paperback. Condition: Very Good. This book is in very good condition; no remainder marks. It does have some cover shelfwear and corner creasing. Inside pages are clean. ; Advances In Computer Vision And Pattern Recognition; 155 X 0.73 X 235 inches; 324 pages.
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From Deep Learning to Graph Neural Networks: Models and Practice(Chinese Edition)
Language: English
Published by Electronic Industry Press, 2023
ISBN 10: 7121456826 ISBN 13: 9787121456824
paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2023-06 Pages: 340 Publisher: Publishing House of Electronic Industry In recent years. deep learning has played a pivotal role in the development of artificial intelligence. and graph neural network is an emerging direction in the field of artificial intelligence. known as deep learning on graphs. This book introduces in detail the basic concepts and cutting-edge technologies from deep learning to graph neural networks. including deep learning on graphs. .
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Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks (Advances in Computer Vision and Pattern Recognition)
Book 75 of 86: Advances in Computer Vision and Pattern RecognitionSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 126.63
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Condition: As New. Unread book in perfect condition.
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Unsupervised Learning in Space and Time : A Modern Approach for Computer Vision Using Graph-based Techniques and Deep Neural Networks
Book 75 of 86: Advances in Computer Vision and Pattern RecognitionSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Unsupervised Learning in Space and Time : A Modern Approach for Computer Vision Using Graph-based Techniques and Deep Neural Networks
Book 75 of 86: Advances in Computer Vision and Pattern RecognitionSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Taschenbuch. Condition: Neu. Unsupervised Learning in Space and Time | A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks | Marius Leordeanu | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xxiii | Englisch | 2021 | Springer | EAN 9783030421304 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Unsupervised Learning in Space and Time : A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks
Book 75 of 86: Advances in Computer Vision and Pattern RecognitionLanguage: English
Published by Springer International Publishing, 2021
ISBN 10: 3030421309 ISBN 13: 9783030421304
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field.Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
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Unsupervised Learning in Space and Time : A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks
Book 75 of 86: Advances in Computer Vision and Pattern RecognitionLanguage: English
Published by Springer International Publishing, Springer Nature Switzerland, 2020
ISBN 10: 3030421279 ISBN 13: 9783030421274
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field.Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
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Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision Using Graph-based Techniques and Deep Neural Networks
Book 75 of 86: Advances in Computer Vision and Pattern RecognitionLanguage: English
Published by Springer-Nature New York Inc, 2021
ISBN 10: 3030421309 ISBN 13: 9783030421304
Paperback. Condition: Brand New. 324 pages. 9.25x6.10x0.77 inches. In Stock.
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Hardcover. Condition: Brand New. 321 pages. 9.25x6.10x0.87 inches. In Stock.
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Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condition: New.
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Condition: New.
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Condition: New.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Implement state-of-the-art graph neural network architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description:Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What You Will Learn:Understand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is for:This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.
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Taschenbuch. Condition: Neu. Hands-On Graph Neural Networks Using Python | Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch | Maxime Labonne | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781804617526 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Hardcover. Condition: Brand New. 321 pages. 9.25x6.10x0.87 inches. In Stock. This item is printed on demand.
