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+---- Téma: Applied Deep Learning on Graphs Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures (/showthread.php?tid=200414)
RE: Applied Deep Learning on Graphs Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectur - book24h - 2024-12-21
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Free Download Applied Deep Learning on Graphs: Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures by Lakshya Khandelwal, Subhajoy Das
English | December 27th, 2024 | ISBN: 1835885977 | 250 pages | True PDF | 19.68 MB
Gain a deep understanding of applied deep learning on graphs from data, algorithm, and engineering viewpoints to construct enterprise-ready solutions using deep learning on graph data for wide range of domains
Key Features
- Explore Graph Data in real-world systems and leverage Graph Learning for impactful business results
- Dive deep into popular and specialized graph Deep neural architectures
- Learn to build scalable and Productionizable Graph Learning solutions
This book provides a comprehensive journey into graph neural networks, guiding readers from foundational concepts all the way to advanced techniques and cutting-edge applications. We begin by motivating why graph data structures are ubiquitous in the era of interconnected information, and why we require specialized deep learning approaches, explaining challenges and with existing methods. Next, readers learn about early graph representation techniques like DeepWalk and node2vec which paved the way for modern advances. The core of the book dives deep into popular graph neural architectures - from essential concepts in graph convolutional and attentional networks to sophisticated autoencoder models to leveraging LLMs and technologies like Retrieval augmented generation on Graph data. With strong theoretical grounding established, we then transition to practical implementations, covering critical topics of scalability, interpretability and key application domains like NLP, recommendations, computer vision and more. By the end of this book, readers master both underlying ideas and hands-on coding skills on real-world use cases and examples along the way. Readers grasp not just how to effectively leverage graph neural networks today but also the promising frontiers to influence where the field may evolve next.
Who is this book for?
For data scientists, machine learning practitioners, researchers delving into graph-based data, and software engineers crafting graph-related applications, this book offers theoretical and practical guidance with real-world examples. A foundational grasp of ML concepts and Python is presumed.
What you will learn
- Discover extracting business value through a graph-centric approach
- Develop a basic intuition of learning graph attributes using Machine Learning
- Explore limitations of traditional Deep Learning with graph data and delve into specialized graph-based architectures
- Learn how Graph Deep Learning finds applications in industry, including Recommender Systems, NLP, etc
- Grasp challenges in production such as scalability and interpretability
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