Deep Learning Graph Neural (41 results)

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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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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Taschenbuch. Condition: Neu. Neuware - Reactive PublishingDiscover the future of molecular discovery with the power of Graph Neural Networks and Geometric Deep Learning.In Graph Neural Networks for Molecular Discovery with Python, Livia Arden delivers a practical, hands-on guide to applying cutting-edge geometric deep learning techniques to one of the most exciting frontiers in science: accelerating the design and optimization of new molecules. Whether you're working in drug discovery, materials science, or chemical engineering, this book equips you with the tools to model molecular structures as graphs and extract powerful insights that traditional methods simply cannot match.What You'll Learn- Master the fundamentals of Graph Neural Networks (GNNs) and how they naturally represent atoms, bonds, and molecular geometry.- Build and train sophisticated models for property prediction - from solubility and toxicity to bioactivity and quantum mechanical properties.- Explore molecule generation techniques, including variational autoencoders, generative adversarial networks, and diffusion models adapted for graphs.- Implement real-world workflows using Python libraries such as PyTorch Geometric, DGL, RDKit, and NetworkX.- Tackle challenges like molecular featurization, graph pooling, attention mechanisms, and scalable training on large chemical datasets.- Apply advanced topics including equivariant networks, 3D molecular modeling, and multi-task learning for accelerated virtual screening.Hands-On and Production-ReadyEvery concept is reinforced with clean, well-documented Python code examples that you can immediately adapt to your own research or projects. From loading SMILES strings and building molecular graphs to deploying predictive models and generating novel candidate compounds, this book bridges theory and practice with a strong emphasis on reproducibility and real-world impact.Who This Book Is For- Data scientists and machine learning engineers eager to apply GNNs to scientific domains.- Computational chemists and researchers looking to modernize their discovery pipelines.- Graduate students and professionals in cheminformatics, bioinformatics, and materials informatics.- Anyone interested in the intersection of geometric deep learning and molecular science.…

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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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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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.…

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.…

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.…

Paperback. Condition: Brand New. 324 pages. 9.25x6.10x0.77 inches. In Stock.

Hardcover. Condition: Brand New. 321 pages. 9.25x6.10x0.87 inches. In Stock.

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