Deep Learning Graph Neural (40 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.

Condition: good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

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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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Condition: New. In English.

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Taschenbuch. Condition: Neu. Neuware - Your first multi-agent RL project will teach you a hard truth: everything you know about single-agent training breaks the moment a second learner enters the room.Non-stationarity sets in. Rewards stop meaning what you think they mean. And the fixes that worked for a single policy quietly make things worse.This is the book for engineers and researchers who already know single-agent RL and are ready for what comes next - written by a practitioner who's built coordinating robot fleets, adversarial trading agents, and cooperating LLM agent teams, and who still remembers exactly where it went wrong the first time.Inside, you'll learn: - Why non-stationarity is the real enemy of MARL - and how to design around it- How to formulate state, observation, action, and reward before you write training code (the highest-leverage decision in any MARL project)- Cooperative methods: value decomposition (VDN, QMIX), credit assignment, and learned communication- Competitive methods: self-play, opponent modeling, exploitability, and why average return lies to you- Scaling to dozens or hundreds of agents without training collapsing- Graph neural networks, mean-field methods, and attention-based communication architectures- Real deployment: sim-to-real transfer, robotics, swarms, and multi-agent LLM systems- Where the field is still unsolved - continual learning, human-AI teams, and multi-agent alignmentWritten in first person, with real mistakes included, not just the theory that made it into the papers. Every chapter builds a working intuition, then shows you exactly how it fails in practice - so you find out in the book, not three weeks into a training run.If you've trained a MARL system, watched it behave strangely, and wanted to know why - this book is for you.…

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: 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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Condition: As New. Unread book in perfect condition.

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Condition: New. In English.

Condition: New. In English.

Condition: As New. Unread book in perfect condition.

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. 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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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.

Paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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