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    Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Lakshya Khandelwal, Subhajoy Das
    • Niedostępna
    Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures Lakshya Khandelwal, Subhajoy Das - okladka książki

    Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures Lakshya Khandelwal, Subhajoy Das - okladka książki

    Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures Lakshya Khandelwal, Subhajoy Das - audiobook MP3

    Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures Lakshya Khandelwal, Subhajoy Das - audiobook CD

    Serie wydawnicze:
    Hands-on
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    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.

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    O autorach książki

    Lakshya is currently leading several Natural Language, forecasting and recommendation system initiatives in Walmart, building next generation AI products for Millions of customers. Lakshya holds a Bachelors and Masters degree from IIT Kanpur in Mathematics and Computer Science and has 8+ years of experience in building Scalable Machine Learning Products for multiple Tech Giants. Before joining Walmart, Lakshya worked as a Data Scientist with Adobe, building Search Bid optimization solutions as part of the advertising cloud suite with major enterprises across the globe as customers. Prior to Adobe, he worked as a Lead ML Engineer with Samsung building Natural Language intelligence for the very first version of Bixby used by millions of users daily.
    Subhajoy is a Staff Data Scientist with seven years of experience under his belt. He graduated from IIT Kharagpur with a Bachelors and Masters degree in Mathematics and Computing. Since then, he has worked in organizations at varying stages of growth: from fast growing e-commerce startups like Meesho to behemoths like Adobe. He has drivennseveral pivotal features in every company he has worked in, like building an end-to-end recommendation system for the Meesho app, curating interesting advertising using Reinforcement Learning based optimizations in Adobe Advertising. He is currently working at Arista Networks, building AI driven apps responsible for cybersecurity of several Fortune 500 companies.

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