Wydawca: Packt Publishing

PrimeFaces Theme Development. Create, package, and customize stunning themes using PrimeFaces

Andrew Mark R Bailey, Davi Ferreira, Andy...

Developing stunning themes for web applications has never been easier! PrimeFaces delivers a powerful set of features that enables JSF developers to create and customize awesome themes on the web. It is very easy to use because it comes as a single JAR file and requires no mandatory XML configuration. With more than 30 out-of-the-box themes, jQuery integration, a mobile UI toolkit, Ajax Push technology, and much more, PrimeFaces takes JSF application development to a whole new level!This book is a hands-on example-rich guide to creating and customizing PrimeFaces themes using available tools.Beginning with creating a JSF project and integrating the PrimeFaces library, this book will introduce you to the features of theme components, how these are structured, and how PrimeFaces uses JQuery UI to apply a theme to your application. You will learn to examine and change the CSS rules and get creative by setting standard icons and adding new icons to them. You will use a combination of JavaScript and CSS to enhance your application with help of scheduler component and go on to adapt and package your custom theme so that it is compatible with the Resource Manager.Finally, you will explore PrimeFaces mobile apps, ensuring themes are compatible with your mobile applications best practices for theme design.

Principles of Data Fabric. Become a data-driven organization by implementing Data Fabric solutions efficiently

Sonia Mezzetta

Data can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates a unified view of data. This book will enable you to design a Data Fabric solution by addressing all the key aspects that need to be considered.The book begins by introducing you to Data Fabric architecture, why you need them, and how they relate to other strategic data management frameworks. You’ll then quickly progress to grasping the principles of DataOps, an operational model for Data Fabric architecture. The next set of chapters will show you how to combine Data Fabric with DataOps and Data Mesh and how they work together by making the most out of it. After that, you’ll discover how to design Data Integration, Data Governance, and Self-Service analytics architecture. The book ends with technical architecture to implement distributed data management and regulatory compliance, followed by industry best practices and principles.By the end of this data book, you will have a clear understanding of what Data Fabric is and what the architecture looks like, along with the level of effort that goes into designing a Data Fabric solution.

Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition

Sinan Ozdemir

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

Principles of Data Science. Mathematical techniques and theory to succeed in data-driven industries

Sinan Ozdemir

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

Principles of Data Science. Understand, analyze, and predict data using Machine Learning concepts and tools - Second Edition

Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas.Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.

Principles of Strategic Data Science. Creating value from data, big and small

Peter Prevos

Mathematics and computer science form an integral part of data science, and understanding them is crucial for efficiently managing data. This book is designed to take you through the entire data science pipeline and help you join the dots between mathematics, programming, and business analysis. You’ll start by learning what data science is and how organizations can use it to revolutionize the way they use their data. The book then covers the criteria for the soundness of data products and demonstrates how to effectively visualize information. As you progress, you’ll discover the strategic aspects of data science by exploring the five-phase framework that enables you to enhance the value you extract from data. Toward the concluding chapters, you’ll understand the role of a data science manager in helping an organization take the data-driven approach.By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data.

Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Srinivasa Rao Aravilli, Sam Hamilton

– In an era of evolving privacy regulations, compliance is mandatory for every enterprise – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks

Privilege Escalation Techniques. Learn the art of exploiting Windows and Linux systems

Alexis Ahmed

Privilege Escalation Techniques is a detailed guide to privilege escalation techniques and tools for both Windows and Linux systems. This is a one-of-a-kind resource that will deepen your understanding of both platforms and provide detailed, easy-to-follow instructions for your first foray into privilege escalation. The book uses virtual environments that you can download to test and run tools and techniques. After a refresher on gaining access and surveying systems, each chapter will feature an exploitation challenge in the form of pre-built virtual machines (VMs). As you progress, you will learn how to enumerate and exploit a target Linux or Windows system. You’ll then get a demonstration on how you can escalate your privileges to the highest level.By the end of this book, you will have gained all the knowledge and skills you need to be able to perform local kernel exploits, escalate privileges through vulnerabilities in services, maintain persistence, and enumerate information from the target such as passwords and password hashes.