A Gentle Introduction To Graph Neural Networks Pdf
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A Hands-On Introduction to Machine Learning
Author: Chirag Shah
language: en
Publisher: Cambridge University Press
Release Date: 2022-12-29
Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Beyond AI
This book explores the transformative potential of ChatGPT, Web3, and their impact on productivity and various industries. It delves into Generative AI (GenAI) and its representative platform ChatGPT, their synergy with Web3, and how they can revolutionize business operations. It covers the potential impact surpassing prior industrial revolutions. After providing an overview of GenAI, ChatGPT, and Web3, it investigates business applications in various industries and areas, such as product management, finance, real estate, gaming, and government, highlighting value creation and operational revolution through their integration. It also explores their impact on content generation, customer service, personalization, and data analysis and examines how the technologies can enhance content quality, customer experiences, sales, revenue, and resource efficiency. Moreover, it addresses security, privacy, and ethics concerns, emphasizing the responsible implementation of ChatGPT and Web3. Written by experts in this field, this book is aimed at business leaders, entrepreneurs, students, investors, and professionals who are seeking insights into ChatGPT, ChatGPT Plug-in, GPT-based autonomous agents, and the integration of Gen AI and Web3 in business applications.
Graph Representation Learning
Author: William L. Hamilton
language: en
Publisher: Springer Nature
Release Date: 2022-06-01
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.