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Total Size:
20.5 MB
Info Hash:
A405B459366F8211D6B189DDA24EB30BE435E025
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Added:
April 21, 2026, 5:05 p.m.
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(Last updated: April 21, 2026, 5:05 p.m.)
| File | Size |
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| ['Code.zip'] | 0 bytes |
| ['Das S. Applied Deep Learning on Graphs. Leverage graph data...2024.pdf'] | 0 bytes |
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337.5 MB
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| Uploaded by indexFroggy | Size 32.0 MB | Health [ 10 /1 ] | Added 2024-11-22 |
NOTE
SOURCE: Das S. Applied Deep Learning on Graphs. Leverage graph data...2024
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COVER
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MEDIAINFO
Textbook in PDF format Key Features: Explore graph data in real-world systems and leverage graph learning for impactful business results Dive into popular and specialized deep neural architectures like graph convolutional and attention networks Learn how to build scalable and productionizable graph learning solutions Book Description: With their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs). This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision. By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies. What you will learn: Discover how to extract business value through a graph-centric approach Develop a basic understanding of learning graph attributes using machine learning Identify the limitations of traditional deep learning with graph data and explore specialized graph-based architectures Understand industry applications of graph deep learning, including recommender systems and NLP Identify and overcome challenges in production such as scalability and interpretability Perform node classification and link prediction using PyTorch Geometric Who this book is 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. Table of Contents: Introduction to Graph Learning Graph Learning in the Real World Graph Representation Learning Deep Learning Models for Graphs Graph Deep Learning Challenges Harnessing Large Language Models for Graph Learning Graph Deep Learning in Practice Graph Deep Learning for Natural Language Processing Building Recommendation Systems Using Graph Deep Learning Graph Deep Learning for Computer Vision Emerging Applications The Future of Graph Learning
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