Wydawca: Packt Publishing
Kunal Dabir, Abhinandan Maheshwari
Gradle is an advanced and modern build automation tool. It inherits the best elements of the past generation of build tools, but it also differs and innovates to bring terseness, elegance, simplicity, and the flexibility to build.Right from installing Gradle and writing your first build file to creating a fully-fledged multi-module project build, this book will guide you through its topics in a step-by-step fashion.You will get your hands dirty with a simple Java project built with Gradle and go on to build web applications that are run with Jetty or Tomcat. We take a unique approach towards explaining the DSL using the Gradle API, which makes the DSL more accessible and intuitive. All in all, this book is a concise guide to help you decipher the Gradle build files, covering the essential topics that are most useful in real-world projects. With every chapter, you will learn a new topic and be able to readily implement your build files.
Grails 1.1 Web Application Development. Reclaiming Productivity for faster Java Web Development
Jon Dickinson
Web development is trickyóeven a simple web application has a number of context changes ready to trip up the unwary. Grails takes the everyday pain out of web application development, allowing us to focus on delivering real application logic and create seamless experiences that will address the needs of our users. This book will take the pain out of Grails by showing you exactly how to build a web application with a minimum of fuss.With this book, even if you are new to Grails, you will be up and running before you know it. You will be able to code faster and your code will be better. This clear and concise book is packed with examples and clear instructions to help you build your first Grails application and gives you the skills to speed up your application development by adding a different angle for learning about the topic. After a brief introduction to the dynamic JVM-based Groovy programming language, which teaches you enough about Groovy to understand the relationship between Grails and the Groovy scripting language, it shows how to use Grails and a number of key plug-ins to deliver valuable web applications. It also takes you through creating, developing, testing, and deploying an example team collaboration application in Grails.Using an incremental and iterative approach you will learn how to build a basic web application with secure authentication and different levels of authorization. You will learn how to handle file upload allowing users to share files. Some advanced features of object-oriented persistence will be introduced through adding tags for messages and files to giving users a robust categorization system.You will then build on the basic application to enhance the user experience through AJAX and the RichUI plug-in. You will take a further step into the world of Web 2.0 by adding an RSS feed and a REST service to the application. Once the entire application is up and running, you will learn how to create your own plug-in for tagging. Finally, you will learn how to deploy this application to a production environment.
Gary Hutson, Matt Jackson
Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis.Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements.By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.
Ravindranatha Anthapu
While it is easy to learn and understand the Cypher declarative language for querying graph databases, it can be very difficult to master it. As graph databases are becoming more mainstream, there is a dearth of content and guidance for developers to leverage database capabilities fully. This book fills the information gap by describing graph traversal patterns in a simple and readable way.This book provides a guided tour of Cypher from understanding the syntax, building a graph data model, and loading the data into graphs to building queries and profiling the queries for best performance. It introduces APOC utilities that can augment Cypher queries to build complex queries. You’ll also be introduced to visualization tools such as Bloom to get the most out of the graph when presenting the results to the end users.After having worked through this book, you’ll have become a seasoned Cypher query developer with a good understanding of the query language and how to use it for the best performance.
Estelle Scifo
Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance.Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline.By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.
Aldo Marzullo, Enrico Deusebio, Claudio Stamile
Graph 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.*Email sign-up and proof of purchase required -
Claudio Stamile, Aldo Marzullo, Enrico Deusebio
Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You’ll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you’ll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You’ll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.