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Introduction to Graph Neural Networks

comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN;

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Introduction to Graph Neural Networks

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with;

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Graph Representation Learning

applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism;

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PyTorch Recipes

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch;

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Advanced Deep Learning with Python

). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural;

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Introduction to Neural Networks for Java, Second Edition

"Introduction to Neural Networks for Java" introduces the Java programmer to the world of Neural Networks and Artificial Intelligence;

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Handbook on Neural Information Processing

applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks;

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Graph Data Mining

techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining;

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Fundamentals Neural NetworksUnited State

An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind;

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Neural Network Methods in Natural Language Processing

computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary;

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Neural Network Methods in Natural Language Processing

computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary;

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Introduction to Deep Learning and Neural Networks with PythonT

Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to;

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Neural Information Processing

from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based;

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Graph Theory and Interconnection Networks

of these interrelated topics. After a brief introduction to graph terminology, the book presents well-known interconnection networks as examples;

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Graph Theory and Interconnection Networks

of these interrelated topics. After a brief introduction to graph terminology, the book presents well-known interconnection networks as examples;

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Blind Equalization in Neural Networks

The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms;

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Introduction to Neural Networks for C#, 2nd Edition

This resource introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Training techniques, such as;

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Statistical Mechanics of Neural Networks

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks;

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Deep Learning from Scratch Building with Python from First Principles

along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer;

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Data Mining

, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors;

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Machine Learning for Computer Scientists and Data Analysts

, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book;

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Artificial Neural Networks in Food Processing

comprehensive overview of ANNs including an introduction to the topic, classifications of single neurons and neural networks, model predictive control;

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Deep Learning on Graphs

and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and;

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Naturally Intelligent Systems

new entry in this arena - neural networks. Naturally Intelligent Systems offers a comprehensive introduction to these exciting systems. It;

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Mathematical Aspects of Spin Glasses and Neural Networks

Aimed at graduates and potential researchers, this is a comprehensive introduction to the mathematical aspects of spin glasses and neural;

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Mathematical Aspects of Spin Glasses and Neural Networks

Aimed at graduates and potential researchers, this is a comprehensive introduction to the mathematical aspects of spin glasses and neural;

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Neural Networks

This introduction to neural networks describes what they are, what they can do and how they do it. While some scientific background;

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