Applied Deep Learning on Graphs | Leverage graph data for business applications using specialized deep learning architectures

Name: Applied Deep Learning on Graphs | Leverage graph data for business applications using specialized deep learning architectures
Author: Lakshya Khandelwal (u. a.)
ISBN: 9781835885963

Lakshya Khandelwal (u. a.)

Lingua: inglese

Editore: Packt Publishing, 2024

1835885969 / 9781835885963

Brossura
Nuovo

Visualizza tutti i dettagli

Da: preigu, Osnabrück, Germaniapreigu

Venditore con 5 stelle

Venditore AbeBooks dal 5 agosto 2024

Visualizza gli articoli di questo venditore

Brossura

Condizione: Nuovo

EUR 84,90

EUR 70,00 spedizione

Spedito da Germania a U.S.A.

Quantità: 5 disponibili

Aggiungi al carrello

Resi gratuiti per 30 giorni

Lista dei preferitiCrea un desiderata per articoli simili

Descrizione dell’articolo da parte del venditore

Codice articolo 130868765

Contatta il venditore per questo articolo

Segnala questo articolo

Titolo: Applied Deep Learning on Graphs | Leverage graph data for business applications using specialized deep learning architectures
Autore: Lakshya Khandelwal (u. a.)
Editore: Packt Publishing
Anno di pubblicazione: 2024
Condizione: Neu
Rilegatura: Taschenbuch
Lingua: inglese
ISBN 10: 1835885969
ISBN 13: 9781835885963
Peso dell'articolo: 474 grammi
Dimensioni: 235 x 191 x 14 mm
Cataloghi dei venditori: Bücher

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 into popular and specialized deep neural architectures like graph convolutional and attention networks
Learn how to build scalable and productionizable graph learning solutions
Purchase of the print or Kindle book includes a free PDF eBook

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.

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

"Riassunto" può appartenere a un’altra edizione di questo titolo.

preigu

Osnabrück, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 5 agosto 2024

Tariffe di spedizione da Germania a U.S.A.

Articolo	Da 60 a 60 giorni lavorativi	Da 60 a 60 giorni lavorativi
Primo articolo	EUR 70,00	EUR 70,00

I tempi di consegna sono stabiliti dai venditori e variano in base al corriere e al paese. Gli ordini che devono attraversare una dogana possono subire ritardi e spetta agli acquirenti pagare eventuali tariffe o dazi associati. I venditori possono contattarti in merito ad addebiti aggiuntivi dovuti a eventuali maggiorazioni dei costi di spedizione dei tuoi articoli.

Metodi di pagamento

PayPal

Descrizione dello Store

preigu betreibt einen Onlineversandhandel mit über 1 Mio. Produkten in verschiedenen Sortimenten. Das Kernsortiment besteht aus Büchern, Medien und Spielwaren. Ein gelungenes Einkaufserlebnis ist das Ziel einer jeden Bestellung bei preigu, denn der Kunde und seine Zufriedenheit stehen an erster Stelle. preigu setzt daher auf einen kompetenten Kundenservice, funktionierende Prozesse und schnelle Reaktion.

Specializzazione

Bücher, Spielwaren

Informazioni sull’azienda del venditore

preigu GmbH & Co. KG

Lengericher Landstraße 19
Osnabrück, Germania 49078

Contatta il venditoreVisualizza gli articoli di questo venditoreVisita la vetrina del venditore

Condizioni di vendita

About Us

Legal website operator identification:

preigu GmbH & Co. KG
Lengericher Landstr. 19
49078 Osnabrück
Germany

Telephone: +49 (0) 541 / 580 72 84
Email: mail@preigu.de

VAT No: DE 455 380 498
AG Osnabrück - HRA 209647

PhG: preigu Verwaltung GmbH
AG Osnabrück - HRB 221793
CEO: Ansas Meyer

We are neither willing nor obliged to participate in dispute resolution proceedings before consumer arbitration boards.

We are a member of the initiative "FairCommerce" since 30.11.2016.
For more information, see: https://www.haendlerbund.de/de/haendlerbund/interessenvertretung/faircommerce

Diritto di recesso

Instructions for revocation

Right of withdrawal for the sale of goods

Revocation right for consumers
(A ‘consumer' is any natural person who concludes a legal transaction which, to an overwhelming extent, cannot be attributed to either his commercial or independent professional activities.)

Instructions for revocation

Revocation right
You have the right to revoke this contract within 14 days without specifying any reasons.
The revocation period is 14 days with effect from the day,

on which you or a third party nominated by you, which is not the carrier, had taken possession of the products, provided you had ordered one or more products within the scope of a standard order and this/these product/products is/are delivered uniformly;
on which you or a third party nominated by you, which is not the carrier, had taken possession of the last product, provided you had ordered several products within the scope of a standard order and these products are delivered separately;
on which you or a third party nominated by you, which is not the carrier, had taken possession of the last part delivery or the last unit, provided you had ordered a product, which is delivered in several part deliveries or units;

To exercise your right of withdrawal, you must inform us (preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, Telephone number: +49 (0) 541 / 580 72 84, E-Mail address: mail@preigu.de) by means of a clear declaration (e.g. a letter sent by post, or an e-mail) of your decision to withdraw from this contract. You can use the attached model withdrawal form for this purpose, which is, however, not mandatory.

You can also exercise your right of withdrawal online by clicking on a button labelled accordingly (such as ‘Withdraw from contract' or similar) on the AbeBooks/ZVAB website. If you use this online function, you will immediately receive a confirmation of receipt on a durable medium (e.g. via email) containing information on the content of the withdrawal notice, as well as the date and time of its receipt.

In order to safeguard the revocation period, it is sufficient that you send the notification about the exercise of the revocation right before the expiry of the revocation period.

Consequences of the revocation

If you revoke this contract, we shall repay all the payments, which we received from you, including the delivery costs (with the exception of additional costs, which arise from that fact that you selected a form of delivery other than the most reasonable standard delivery offered by us), immediately and at the latest within 14 days from the day on which we received the notification about the revocation of this contract from you. We use the same means of payment, which you had originally used during the original transaction, for this repayment unless expressly agreed otherwise with you; you will not be charged any fees owing to this repayment.

We can refuse the repayment until the products are returned to us or until you have furnished evidence that you have sent the products back to us, depending on whichever is earlier.

You must return or transfer the products to us immediately and, in any case, at the latest within 14 days with effect from the day on which you inform us of the revocation of this contract. The deadline is maintained if you send the products before the expiry of the 14 day deadline.

You bear the direct costs for returning the products.

You must pay for any depreciation of the products only if this depreciation can be attributed to any handling with you that was not necessary for checking the condition, features and functionality of the products.

Criteria for exclusion or expiry

The revocation right is not available for contracts

for delivery of products, which are not prefabricated and for whose manufacturing an individual selection or stipulation by the consumer is important or which are clearly tailored to the personal requirements of the consumer;
for delivery of products, which can spoil quickly or whose use-by date would be exceeded quickly;
for delivery of alcoholic drinks, whose price was agreed at the time of concluding the contract, which however can be delivered 30 days after the conclusion of the contract at the earliest and whose current value depends on the fluctuations in the market, on which the entrepreneur has no influence;
for delivery of newspapers, periodicals or magazines with the exception of subscription contracts. The revocation right expires prematurely in case of contracts
for delivery of sealed products, which are not suitable for return for reasons of health protection or hygiene if their seal has been removed after the delivery;
for delivery of products if they have been mixed inseparably with other goods after the delivery, owing to their condition;
for delivery of sound or video recording or computer software in a sealed package if the seal has been removed after the delivery.

Specimen - revocation form

(If you wish to revoke the contract, please fill up this form and send it back to us.)

To preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, Email address: mail@preigu.de :
I/we () herewith revoke the contract concluded by me/ us () regarding the purchase of the following products ()/
the provision of the following service ()
Ordered on ()/ received on ()
Name of the consumer(s)
Address of the consumer(s)
Signature of the consumer(s) (only in case of a notification on paper)
Date

(*) Cross out the incorrect option.