Wydawca: 16
Learning PowerShell DSC. Automate deployment and configuration of your servers - Second Edition
James Pogran
The main goal of this book is to teach you to configure, deploy, and manage your system using the new features of PowerShell v5/v6 DSC.This book begins with the basics of PowerShell Desired State Configuration, covering its architecture and components. It familiarizes you with the set of Windows PowerShell language extensions and new Windows PowerShell commands that make up DSC. Then it helps you create DSC custom resources and work with DSC configurations with the help of practical examples. Finally, it describes how to deploy configuration data using PowerShell DSC. Throughout this book, we will be focusing on concepts such as building configurations with parameters, the local configuration manager, and testing and restoring configurations using PowerShell DSC.By the end of the book, you will be able to deploy a real-world application end-to-end and will be familiar enough with the powerful Desired State Configuration platform to achieve continuous delivery and efficiently and easily manage and deploy data for systems.
Learning Predictive Analytics with Python. Click here to enter text
Ashish Kumar
Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
Eric Mayor
This book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data.You will learn how to perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. All chapters will guide you in acquiring the skills in a practical way. Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further.The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages.
David Bellot, Dan Toomey
Probabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We’ll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we’ll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you’ll see the advantage of going probabilistic when you want to do prediction. Next, you’ll master using R packages and implementing its techniques. Finally, you’ll be presented with machine learning applications that have a direct impact in many fields. Here, we’ll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.
Rik Goldman
Proxmox VE 4.1 provides an open source, enterprise virtualization platform on which to host virtual servers as either virtual machines or containers.This book will support your practice of the requisite skills to successfully create, tailor, and deploy virtual machines and containers with Proxmox VE 4.1. Following a survey of PVE's features and characteristics,this book will contrast containers with virtual machines and establish cases for both. It walks through the installation of Proxmox VE, explores the creation of containers and virtual machines, and suggests best practices for virtual disk creation, network configuration, and Proxmox VE host and guest security.Throughout the book, you will navigate the Proxmox VE 4.1 web interface and explore options for command-linemanagement