Showing results for "Distributed Graph Algorithms for Computer Networks Kayhan Erciyes English"

This book presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Topics and features: introduces a range of fundamental graph algorithms, covering spanning trees, graph traversal algorithms, routing algorithms, and self-stabilization; reviews graph-theoretical distributed approximation algorithms with applications in ad hoc wireless networks; describes in detail the implementation of eac...

Elements of Deep Learning for Computer Vision gives a thorough understanding of deep learning and provides highly accurate computer vision solutions while using libraries like PyTorch.This book introduces you to Deep Learning and explains all the concepts required to understand the basic working, development, and tuning of a neural network using Pytorch. The book then addresses the field of computer vision using two libraries, including the Python wrapper/version of OpenCV and PIL. After e...

Unleash Julia’s power: Code Your Data Stories, Shape Machine Intelligence!Book DescriptionThis book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for der...

What if the most important information in your data lies not in individual rows and columns, but in the connections between them? Graph machine learning helps uncover patterns hidden in these relationships.Graph Machine Learning Essentials is a practical and accessible guide to understanding how machine learning works with graph-structured data, where entities are connected through relationships.Designed for software engineers, ML engineers, data scientists, resear...

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description

“Elevate Your Skills to Master The Cloud”Key Features● Step-by-step guide and real-world scenarios to help readers confidently implement Azure IaaS solutions.● Detailed explanations of Azure IaaS components like virtual machines, storage, and networks, offering a thorough understanding of each element in the cloud infrastructure.● Insights into evolving cloud technologies to keep skills relevant and prepare for advance...

“Elevate Your Skills to Master The Cloud”Book DescriptionUltimate Azure IaaS for Infrastructure Management is your go-to guide for mastering Azure’s Infrastructure as a Service (IaaS). The book demystifies the complexities of cloud architecture, offering readers a clear path to understanding and leveraging Azure’s full potential starting from the basics.The book begins by introducing you to the fundamentals of Azure IaaS, ensuring a solid foundation. As you progress, the book...

The Most Reliable AI Systems Do Not Just Retrieve Information—They Understand RelationshipsRetrieval-Augmented Generation (RAG) works better when it reasons over-connected knowledge, rather than isolated text chunks — and GraphRAG is the architectural approach that makes that possible. Ultimate GraphRAG for AI Engineers is a practical guide to building retrieval systems that combine knowledge graphs, large language models, and graph-native reasoning, taking practitioners from funda...

Complex Networks: An Algorithmic Perspective supplies the basic theoretical algorithmic and graph theoretic knowledge needed by every researcher and student of complex networks. This book is about specifying, classifying, designing, and implementing mostly sequential and also parallel and distributed algorithms that can be used to analyze the static properties of complex networks. Providing a focused scope which consists of graph theory and algorithms for complex networks, the book identif...

This unique textbook/reference presents unified coverage of bioinformatics topics relating to both biological sequences and biological networks, providing an in-depth analysis of cutting-edge distributed algorithms, as well as of relevant sequential algorithms. In addition to introducing the latest algorithms in this area, more than fifteen new distributed algorithms are also proposed. Topics and features: reviews a range of open challenges in biological sequences and networks; describes i...

This book is a vital resource for anyone looking to understand the essential role of graph theory as the unifying thread that connects and provides innovative solutions across a wide spectrum of modern computer science disciplines.Graph theory is a traditional mathematical discipline that has evolved as a basic tool for modeling and analyzing the complex relationships between different technological landscapes. Graph theory helps explain the semantic and syntactic ...

This book constitutes revised selected papers from the 5th International Workshop on Distributed Computing for Emerging Smart Networks, DiCES-N 2025, held in Hammamet, Tunisia, during April 26-27, 2025.The 7 full papers, 2 short papers and 1 invited paper included in this book were carefully reviewed and selected from 24 submissions. These papers focus on following topical sections: Artificial intelligence for safety and security; Artificial intelligence for e-health.