Ehrig H Graph and Model Transformation General Framework and Applications 2016 | 15.54 MB
N/A | 468 Pages

Title: TruSkin Vitamin C Serum for Face & Eye Area, Anti Aging Serum with Hyaluronic Acid, Vitamin E, Organic Aloe Vera and Jojoba Oil, Hydrating & Brightening Serum for Dark Spots, Fine Lines and Wrinkles, 1 fl oz
Author: N/A
Year: N/A

Ehrig H Fundamentals of Algebraic Graph Transformation 2006 | 11.63 MB
English | 373 Pages

Title: Osmo - Pizza CoAges 5-12 - Communication Skills & Math - Educational Learning GameSTEM Toy - Gifts for Kids, Boy & GirAges 5 6 7 8 9 10 11 12 - For iPad or Fire Tablet (Osmo Base Required)
Author: H. Ehrig
Year: 2006

Heckel R Graph Transformation, Specifications, and Nets 2018 | 18.32 MB
N/A | 343 Pages

Title: 464263_Print.indd
Author: N/A
Year: 2014

Voloshin V Introduction to Graph and Hypergraph Theory 2009 | 9.21 MB
N/A | 303 Pages

Title: Introduction to Graph and Hypergraph Theory
Author: Voloshin, Vitaly I.(Editor)
Year: N/A

Shi C Advances in Graph Neural Networks 2023 | 5.46 MB
N/A | 207 Pages

Title: The Fidget Game Learn to Read in Weeks Master 220 High-Frequency Dolch Sight Words Curriculum-Appropriate for Pre-K to Grade 3 - Popping Mats & Dice
Author: N/A
Year: N/A

Published 11/2022
Created by Md. A. Barik
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 24 Lectures ( 1h 55m ) | Size: 652 MB

English | 2023 | ISBN: 3031161734 | 207 pages | True PDF EPUB | 31.2 MB
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

English | 2022 | ISBN: 978-1032224565 | 272 pages | True PDF | 11.25 MB
Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models.
Features:

Duration: 1h 34m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 183 MB
Genre: eLearning | Language: English
This course will teach you how to create and represent graph data using GraphFrames in Apache Spark and implement graph algorithms such as Shortest Path and PageRank on Azure Databricks.
What you'll learn

Garg M Graph Learning and Network Science for NLP 2022 | 11.25 MB
N/A | 272 Pages

Title: Graph Learning and Network Science for Natural Language Processing
Author: Edited by Muskan Garg
Year: N/A