Neural Network Fundamentals with Graphs, Algorithms, and Applications PDF Download

Author: Nirmal K. Bose
Publisher: McGraw-Hill Companies
ISBN:
Category : Computers
Languages : en
Pages : 520

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Book Description

Author: Lingfei Wu
Publisher: Springer Nature
ISBN: 9811660549
Category : Computers
Languages : en
Pages : 701

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Book Description
Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

Author: Laurene V. Fausett
Publisher: Prentice Hall
ISBN: 9780133341867
Category : Computers
Languages : en
Pages : 461

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Book Description
Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.

Author: Zhiyuan Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681737663
Category : Computers
Languages : en
Pages : 129

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Book Description
This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.

Author: Laurene V. Fausett
Publisher: Pearson Education India
ISBN: 9788131700532
Category : Neural networks (Computer science)
Languages : en
Pages : 472

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Book Description

Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319944630
Category : Computers
Languages : en
Pages : 497

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Book Description
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141

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Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 97

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Book Description
"Graph Prompting" explores the intersection of Graph Neural Networks (GNNs) and prompt engineering, providing a comprehensive guide on leveraging these technologies for advanced AI applications. The book is structured into several key sections, each delving into different aspects of graph-based AI. #### Fundamentals of Graph Theory The book begins by laying the foundation with essential concepts in graph theory, such as nodes, edges, types of graphs, and graph representations. It explains fundamental metrics like degree, centrality, and clustering coefficients, and covers important algorithms for pathfinding and connectivity. #### Introduction to Prompting The next section introduces prompting in AI, particularly for large language models (LLMs). It covers the basics of prompt engineering, types of prompts (instruction-based, task-based), and design principles. Techniques like contextual prompting, chain-of-thought prompting, and few-shot/zero-shot prompting are discussed, providing practical examples and use cases. #### Graph Neural Networks (GNNs) A comprehensive overview of GNNs follows, detailing their architecture and applications. Key models like Graph Convolutional Networks (GCNs), GraphSAGE, and Graph Attention Networks (GATs) are explained with examples. The section also covers advanced GNN models, including transformer-based graph models and attention mechanisms. #### Graph Prompting for LLMs This section focuses on integrating GNNs with LLMs. It explores techniques for using graph embeddings in prompting, enhancing the capabilities of LLMs in various tasks such as recommendation systems, anomaly detection, and question answering. Practical applications and case studies demonstrate the effectiveness of these integrations. #### Ethics and Fairness in Graph Prompting Ethical considerations are crucial, and the book addresses biases in graph data and fairness in graph algorithms. It discusses the ethical implications of using graph data and provides strategies to ensure fairness and mitigate biases. #### Practical Applications and Case Studies The book highlights real-world applications of graph prompting in healthcare, social networks, and recommendation systems. Each case study showcases the practical benefits and challenges of implementing these technologies in different domains. #### Implementation Guides and Tools For practitioners, the book offers step-by-step implementation guides, using popular libraries like PyTorch Geometric and DGL. Example projects provide hands-on experience, helping readers apply the concepts discussed. #### Future Trends and Conclusion The book concludes with a look at future trends in graph prompting, including scalable GNNs, graph-based reinforcement learning, and ethical AI. It encourages continuous exploration and adaptation to leverage the full potential of graph-based AI technologies. Overall, "Graph Prompting" is a detailed and practical guide, offering valuable insights and tools for leveraging GNNs and prompt engineering to advance AI applications across various domains.

Author: M. Ananda Rao
Publisher: Alpha Science Int'l Ltd.
ISBN: 9781842651315
Category : Artificial intelligence
Languages : en
Pages : 260

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Book Description

Author: Sebastian Klaas
Publisher: BPB Publications
ISBN: 9389423716
Category : Computers
Languages : en
Pages : 300

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Book Description
KEY FEATURES ● Understand applications like reinforcement learning, automatic driving and image generation. ● Understand neural networks accompanied with figures and charts. ● Learn about determining coefficients and initial values of weights. DESCRIPTION Deep learning helps you solve issues related to data problems as it has a vast array of mathematical algorithms and has capacity to detect patterns. This book starts with a quick view of deep learning in Python which would include definition, features and applications. You would be learning about perceptron, neural networks, Backpropagation. This book would also give you a clear insight of how to use Numpy and Matplotlin in deep learning models. By the end of the book, you’ll have the knowledge to apply the relevant technologies in deep learning. WHAT YOU WILL LEARN ● To develop deep learning applications, use Python with few outside inputs. ● Study several ideas of profound learning and neural networks ● Learn how to determine coefficients of learning and weight values ● Explore applications such as automation, image generation and reinforcement learning ● Implement trends like batch Normalisation, dropout, and Adam WHO THIS BOOK IS FOR Deep Learning from the Basics is for data scientists, data analysts and developers who wish to build efficient solutions by applying deep learning techniques. Individuals who would want a better grasp of technology and an overview. You should have a workable Python knowledge is a required. NumPy knowledge and pandas will be an advantage, but that’s completely optional. TABLE OF CONTENTS 1. Python Introduction 2. Perceptron in Depth 3. Neural Networks 4. Training Neural Network 5. Backpropagation 6. Neural Network Training Techniques 7. CNN 8. Deep Learning