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    Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures Lakshya Khandelwal, Subhajoy Das

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures Lakshya Khandelwal, Subhajoy Das - okladka książki

    Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures Lakshya Khandelwal, Subhajoy Das - okladka książki

    Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures Lakshya Khandelwal, Subhajoy Das - audiobook MP3

    Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures Lakshya Khandelwal, Subhajoy Das - audiobook CD

    Autorzy:
    Lakshya Khandelwal, Subhajoy Das
    Serie wydawnicze:
    Hands-on
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Stron:
    250
    Zostało Ci na świąteczne zamówienie opcje wysyłki »
    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.

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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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