Windows Server Automation with PowerShell Cookbook. Powerful ways to automate and manage Windows administrative tasks - Fourth Edition

Thomas Lee, Jeffrey Snover

With a foreword from PowerShell creator Jeffrey Snover, this heavily updated edition is designed to help you learn how to use PowerShell 7.1 effectively and manage the core roles, features, and services of Windows Server in an enterprise setting. All scripts are compatible with both Window Server 2022 and 2019.This latest edition equips you with over 100 recipes you'll need in day-to-day work, covering a wide range of fundamental and more advanced use cases. We look at how to install and configure PowerShell 7.1, along with useful new features and optimizations, and how the PowerShell compatibility solution bridges the gap to older versions of PowerShell. Topics include using PowerShell to manage networking and DHCP in Windows Server, objects in Active Directory, Hyper-V, and Azure. Debugging is crucial, so the book shows you how to use some powerful tools to diagnose and resolve issues with Windows Server.

Hands-On Machine Learning with Microsoft Excel 2019. Build complete data analysis flows, from data collection to visualization

Julio Cesar Rodriguez Martino

We have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel.The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed.At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.

OpenSceneGraph 3.0: Beginner's Guide. This book is a concise introduction to the main features of OpenSceneGraph which then leads you into the fundamentals of developing virtual reality applications. Practical instructions and explanations accompany you every step of the way

Xuelei Qian, Rui Wang, Robert Osfield

Virtual reality has quite a lot of demand in computer science today and OpenSceneGraph, being one of the best 3D graphics toolkits, is being used widely. Although you can use the powerful OpenSceneGraph, based on the low-level OpenGL API, to implement virtual-reality applications that simulate different environments in the 3D world, developing picture-perfect applications is easier said than done.This book has been written with the goal of helping readers become familiar with the structure and main functionalities of OpenSceneGraph (OSG), and guide them to develop virtual-reality applications using this powerful 3D graphics engine. This book covers the essence of OpenSceneGraph (OSG), providing programmers with detailed explanations and examples of scene graph APIs.This book helps you take full advantages of the key features and functionalities of OpenSceneGraph (OSG). You will learn almost all of the core elements required in a virtual reality application, including memory management, geometry creation, the structure of the scene graph, realistic rendering effects, scene navigation, animation, interaction with input devices and external user interfaces, file reading and writing, and so on.With the essential knowledge contained in this book, you will be able to start using OSG in your own projects and research fields, and extend its functionalities by referring to OSG's source code, official examples and API documentation.This handy book divides the core functionalities of the proved and comprehensive OpenSceneGraph (OSG) 3D graphics engine into different aspects, which are introduced in separate chapters. Each chapter can be treated as an individual part that covers one important field of OSG programming, along with several examples illustrating concrete usages and solutions. But the sequence of chapters is also organized from the easy to the more difficult, to help you get to grips with OSG.By the end of the whole book, you will have gained a ready-to-use OSG development environment for yourself and have the general ability to develop OSG-based applications and extend practical functionalities for your own purposes.

Hands-On Deep Learning with R. A practical guide to designing, building, and improving neural network models using R

Michael Pawlus, Rodger Devine

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming.This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems.By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.

Learning jQuery. Create better interaction, design, and web development with simple JavaScript techniques

jQuery Foundation, Karl Swedberg, Jonathan Chaffer

To build interesting, interactive sites, developers are turning to JavaScript libraries such as jQuery to automate common tasks and simplify complicated ones. Because many web developers have more experience with HTML and CSS than with JavaScript, the library's design lends itself to a quick start for designers with little programming experience. Experienced programmers will also be aided by its conceptual consistency.Learning jQuery Third Edition is revised and updated for version 1.6 of jQuery. You will learn the basics of jQuery for adding interactions and animations to your pages. Even if previous attempts at writing JavaScript have left you baffled, this book will guide you past the pitfalls associated with AJAX, events, effects, and advanced JavaScript language features.Starting with an introduction to jQuery, you will first be shown how to write a functioning jQuery program in just three lines of code. Learn how to add impact to your actions through a set of simple visual effects and to create, copy, reassemble, and embellish content using jQuery's DOM modification methods. The book will step you through many detailed, real-world examples, and even equip you to extend the jQuery library itself with your own plug-ins.

Groovy 2 Cookbook. Java and Groovy go together like ham and eggs, and this book is a great opportunity to learn how to exploit Groovy 2 to the full. Packed with recipes, both intermediate and advanced, it's a great way to speed up and modernize your programming

Luciano Fiandesio, Andrey Adamovich, Andrey Adamovich And Luciano Fiandesio

Get up to speed with Groovy, a language for the Java Virtual Machine (JVM) that integrates features of both object-oriented and functional programming. This book will show you the powerful features of Groovy 2 applied to real-world scenarios and how the dynamic nature of the language makes it very simple to tackle problems that would otherwise require hours or days of research and implementation.Groovy 2 Cookbook contains a vast number of recipes covering many facets of today's programming landscape. From language-specific topics such as closures and metaprogramming, to more advanced applications of Groovy flexibility such as DSL and testing techniques, this book gives you quick solutions to everyday problems.The recipes in this book start from the basics of installing Groovy and running your first scripts and continue with progressively more advanced examples that will help you to take advantage of the language's amazing features.Packed with hundreds of tried-and-true Groovy recipes, Groovy 2 Cookbook includes code segments covering many specialized APIs to work with files and collections, manipulate XML, work with REST services and JSON, create asynchronous tasks, and more. But Groovy does more than just ease traditional Java development: it brings modern programming features to the Java platform like closures, duck-typing, and metaprogramming.In this new book, you'll find code examples that you can use in your projects right away along with a discussion about how and why the solution works. Focusing on what's useful and tricky, Groovy 2 Cookbook offers a wealth of useful code for all Java and Groovy programmers, not just advanced practitioners.

Practical Cybersecurity Architecture. A guide to creating and implementing robust designs for cybersecurity architects

Ed Moyle, Diana Kelley

Cybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization.With this audiobook, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs.By the end of this audiobook, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.

Data Cleaning and Exploration with Machine Learning. Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Michael Walker

Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.