Download Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures PDF Free - Full Version
Download Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures by Lakshya Khandelwal in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!
About Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures
Gain a deep understanding on how to construct enterprise ready solutions using Deep Learning on Graph Data for wide range of domains. Gain perspective on this emerging field from Data, Algorithm and Engineering viewpoints.Key FeaturesExplore Graph Data in real-world systems and leverage Graph Learning for impactful business resultsDive deep into popular and specialized graph Deep neural architecturesLearn to build scalable and Productionizable Graph Learning solutionsBook DescriptionThis 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.What you will learnDiscover extracting business value through a graph-centric approachDevelop a basic intuition of learning graph attributes using Machine LearningExplore limitations of traditional Deep Learning with graph data and delve into specialized graph-based architecturesLearn how Graph Deep Learning finds applications in industry, including Recommender Systems, NLP, etcGrasp challenges in production such as scalability and interpretabilityWho this book is forFor 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 ContentsIntroduction to Graph LearningGraph Learning in real WorldGraph Representation LearningDeep Learning Models for GraphsGraph Learning ChallengesLarge Language Models for Graph LearningGraph Deep Learning in PracticeGraph Deep Learning for NLPBuilding Recommendation systems using Graph Deep learningGraph Deep Learning for Computer VisionOther ApplicationsLimitations and Future
Detailed Information
| Author: | Lakshya Khandelwal |
|---|---|
| Publication Year: | 2024 |
| ISBN: | 9781835885970 |
| Language: | English |
| File Size: | 3.6 |
| Format: | |
| Price: | FREE |
Safe & Secure Download - No registration required
Why Choose PDFdrive for Your Free Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures Download?
- 100% Free: No hidden fees or subscriptions required for one book every day.
- No Registration: Immediate access is available without creating accounts for one book every day.
- Safe and Secure: Clean downloads without malware or viruses
- Multiple Formats: PDF, MOBI, Mpub,... optimized for all devices
- Educational Resource: Supporting knowledge sharing and learning
Frequently Asked Questions
Is it really free to download Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures PDF?
Yes, on https://PDFdrive.to you can download Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures by Lakshya Khandelwal completely free. We don't require any payment, subscription, or registration to access this PDF file. For 3 books every day.
How can I read Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures on my mobile device?
After downloading Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures PDF, you can open it with any PDF reader app on your phone or tablet. We recommend using Adobe Acrobat Reader, Apple Books, or Google Play Books for the best reading experience.
Is this the full version of Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures?
Yes, this is the complete PDF version of Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures by Lakshya Khandelwal. You will be able to read the entire content as in the printed version without missing any pages.
Is it legal to download Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures PDF for free?
https://PDFdrive.to provides links to free educational resources available online. We do not store any files on our servers. Please be aware of copyright laws in your country before downloading.
The materials shared are intended for research, educational, and personal use in accordance with fair use principles.
