Learning Cypher
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Learning Neo4j
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Author : Rik Van Bruggen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-08-25
Learning Neo4j written by Rik Van Bruggen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-25 with Computers categories.
This book is for developers who want an alternative way to store and process data within their applications. No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily.
Learning Cypher
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Author : Onofrio Panzarino
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-05-14
Learning Cypher written by Onofrio Panzarino and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-14 with Computers categories.
An easy-to-follow guide full of tips and examples of real-world applications. In each chapter, a thorough example will show you the concepts in action, followed by a detailed explanation. This book is intended for those who want to learn how to create, query, and maintain a graph database, or who want to migrate to a graph database from SQL. It would be helpful to have some familiarity with Java and/or SQL, but no prior experience is required.
Learning Neo4j 3 X
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Author : Jerome Baton
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-10-20
Learning Neo4j 3 X written by Jerome Baton and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-20 with Computers categories.
Run blazingly fast queries on complex graph datasets with the power of the Neo4j graph database About This Book Get acquainted with graph database systems and apply them in real-world use cases Use Cypher query language, APOC and other Neo4j extensions to derive meaningful analysis from complex data sets. A practical guide filled with ready to use examples on querying, graph processing and visualizing information to build smarter spatial applications. Who This Book Is For This book is for developers who want an alternative way to store and process data within their applications. No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily. What You Will Learn Understand the science of graph theory, databases and its advantages over traditional databases. Install Neo4j, model data and learn the most common practices of traversing data Learn the Cypher query language and tailor-made procedures to analyze and derive meaningful representations of data Improve graph techniques with the help of precise procedures in the APOC library Use Neo4j advanced extensions and plugins for performance optimization. Understand how Neo4j's new security features and clustering architecture are used for large scale deployments. In Detail Neo4j is a graph database that allows traversing huge amounts of data with ease. This book aims at quickly getting you started with the popular graph database Neo4j. Starting with a brief introduction to graph theory, this book will show you the advantages of using graph databases along with data modeling techniques for graph databases. You'll gain practical hands-on experience with commonly used and lesser known features for updating graph store with Neo4j's Cypher query language. Furthermore, you'll also learn to create awesome procedures using APOC and extend Neo4j's functionality, enabling integration, algorithmic analysis, and other advanced spatial operation capabilities on data. Through the course of the book you will come across implementation examples on the latest updates in Neo4j, such as in-graph indexes, scaling, performance improvements, visualization, data refactoring techniques, security enhancements, and much more. By the end of the book, you'll have gained the skills to design and implement modern spatial applications, from graphing data to unraveling business capabilities with the help of real-world use cases. Style and approach A step-by-step approach of adopting Neo4j, the world's leading graph database. This book includes a lot of background information, helps you grasp the fundamental concepts behind this radical new way of dealing with connected data, and will give you lots of examples of use cases and environments where a graph database would be a great fit
Graph Powered Machine Learning
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Author : Alessandro Negro
language : en
Publisher: Simon and Schuster
Release Date : 2021-10-05
Graph Powered Machine Learning written by Alessandro Negro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-05 with Computers categories.
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs
Building Web Applications With Python And Neo4j
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Author : Sumit Gupta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-07-16
Building Web Applications With Python And Neo4j written by Sumit Gupta and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-16 with Computers categories.
Py2neo is a simple and pragmatic Python library that provides access to the popular graph database Neo4j via its RESTful web service interface. This brings with it a heavily refactored core, a cleaner API, better performance, and some new idioms. You will begin with licensing and installing Neo4j, learning the fundamentals of Cypher as a graph query language, and exploring Cypher optimizations. You will discover how to integrate with various Python frameworks such as Flask and its extensions: Py2neo, Neomodel, and Django. Finally, the deployment aspects of your Python-based Neo4j applications in a production environment is also covered. By sequentially working through the steps in each chapter, you will quickly learn and master the various implementation details and integrations of Python and Neo4j, helping you to develop your use cases more quickly.
Neo4j High Performance
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Author : Sonal Raj
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-03-02
Neo4j High Performance written by Sonal Raj and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-02 with Computers categories.
If you are a professional or enthusiast who has a basic understanding of graphs or has basic knowledge of Neo4j operations, this is the book for you. Although it is targeted at an advanced user base, this book can be used by beginners as it touches upon the basics. So, if you are passionate about taming complex data with the help of graphs and building high performance applications, you will be able to get valuable insights from this book.
Machine Learning A Gateway To Data Science
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Author : Mrs.S.N.Santhalakshmi
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-05-16
Machine Learning A Gateway To Data Science written by Mrs.S.N.Santhalakshmi and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-16 with Computers categories.
Mrs.S.N.Santhalakshmi, Assistant Professor & Head of The Department, Department of Computer Applications, Nandha Arts & Science College, Erode, Tamil Nadu, India. Dr.Goutam Panigrahi, Assistant Professor, Department of Mathematics, National Institute of Technology, Durgapur, West Bengal, India. Dr. Saibal Majumder, Assistant Professor, Department of Computer Science and Engineering(Data Science), Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India. Dr. Chandan Bandyopadhyay, Associate Professor & Head of the Department, Department of Computer Science and Engineering(Data Science), Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India.
Techno Vernacular Creativity And Innovation
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Author : Nettrice R. Gaskins
language : en
Publisher: MIT Press
Release Date : 2021-08-10
Techno Vernacular Creativity And Innovation written by Nettrice R. Gaskins and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-10 with Education categories.
A novel approach to STEAM learning that engages students from historically marginalized communities in culturally relevant and inclusive maker education. The growing maker movement in education has become an integral part of both STEM and STEAM learning, tapping into the natural DIY inclinations of creative people as well as the educational power of inventing or making things. And yet African American, Latino/a American, and Indigenous people are underrepresented in maker culture and education. In this book, Nettrice Gaskins proposes a novel approach to STEAM learning that engages students from historically marginalized communities in culturally relevant and inclusive maker education. Techno-vernacular creativity (TVC) connects technical literacy, equity, and culture, encompassing creative innovations produced by ethnic groups that are often overlooked. TVC uses three main modes of activity: reappropriation, remixing, and improvisation. Gaskins looks at each of the three modes in turn, guiding readers from research into practice. Drawing on real-world examples, she shows how TVC creates dynamic learning environments where underrepresented ethnic students feel that they belong. Students who remix computationally, for instance, have larger toolkits of computational skills with which to connect cultural practices to STEAM subjects; reappropriation offers a way to navigate cultural repertoires; improvisation is firmly rooted in cultural and creative practices. Finally, Gaskins explores an equity-oriented approach that makes a distinction between conventional or dominant pedagogical approaches and culturally relevant or responsive making methods and practices. She describes TVC habits of mind and suggests methods of instructions and projects.
Learning Python
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Author : Fabrizio Romano
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-12-24
Learning Python written by Fabrizio Romano and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-24 with Computers categories.
Learn to code like a professional with Python – an open source, versatile, and powerful programming language Key Features Learn the fundamentals of programming with Python – one of the best languages ever created Develop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Python’s portability Create outstanding applications of all kind, from websites to scripting, and from GUIs to data science Book DescriptionLearning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned. What you will learn Get Python up and running on Windows, Mac, and Linux in no time Grasp the fundamental concepts of coding, along with the basics of data structures and control flow. Write elegant, reusable, and efficient code in any situation Understand when to use the functional or the object oriented programming approach Create bulletproof, reliable software by writing tests to support your code Explore examples of GUIs, scripting, data science and web applications Learn to be independent, capable of fetching any resource you need, as well as dig deeper Who this book is for Python is the most popular introductory teaching language in U.S. top computer science universities, so if you are new to software development, or maybe you have little experience, and would like to start off on the right foot, then this language and this book are what you need. Its amazing design and portability will help you become productive regardless of the environment you choose to work with.
Graph Machine Learning
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Author : Aldo Marzullo
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-18
Graph Machine Learning written by Aldo Marzullo and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Mathematics categories.
Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric, and DGL Key Features Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL) Explore GML frameworks and their main characteristics Leverage LLMs for machine learning on graphs and learn about temporal learning Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGraph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning. The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools. By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.What you will learn Implement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGL Apply graph analysis to dynamic datasets using temporal graph ML Enhance NLP and text analytics with graph-based techniques Solve complex real-world problems with graph machine learning Build and scale graph-powered ML applications effectively Deploy and scale your application seamlessly Who this book is for This book is for data scientists, ML professionals, and graph specialists looking to deepen their knowledge of graph data analysis or expand their machine learning toolkit. Prior knowledge of Python and basic machine learning principles is recommended.