Exploring Experience Design. Fusing business, tech, and design to shape customer engagement

Ezra Schwartz

We live in an experience economy in which interaction with products is valued more than owning them. Products are expected to engage and delight in order to form the emotional bonds that forge long-term customer loyalty:Products need to anticipate our needs and perform tasks for us: refrigerators order food, homes monitor energy, and cars drive autonomously; they track our vitals, sleep, location, finances, interactions, and content use; recognize our biometric signatures, chat with us, understand and motivate us. Beautiful and easy to use, products have to be fully customizable to match our personal preferences.Accomplishing these feats is easier said than done, but a solution has emerged in the form of Experience design (XD), the unifying approach to fusing business, technology and design around a user-centered philosophy.This book explores key dimensions of XD: Close collaboration among interdisciplinary teams, rapid iteration and ongoing user validation. We cover the processes, methodologies, tools, techniques and best-practices practitioners use throughout the entire product development life-cycle, as ideas are transformed to into positive experiences which lead to perpetual customer engagement and brand loyalty.

R Statistics Cookbook. Over 100 recipes for performing complex statistical operations with R 3.5

Francisco Juretig

R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools.You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making.By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.

Testing Practitioner Handbook. Gain insights into the latest technology and business trends within testing domains

Renu Rajani

The book is based on the author`s experience in leading and transforming large test engagements and architecting solutions for customer testing requirements/bids/problem areas. It targets the testing practitioner population and provides them with a single go-to place to find perspectives, practices, trends, tools, and solutions to test applications as they face the evolving digital world. This book is divided into five parts where each part explores different aspects of testing in the real world. The first module explains the various testing engagement models. You will then learn how to efficiently test code in different life cycles. The book discusses the different aspects of Quality Analysis consideration while testing social media, mobile, analytics, and the Cloud. In the last module, you will learn about futuristic technologies to test software.By the end of the book, you will understand the latest business and IT trends in digital transformation and learn the best practices to adopt for business assurance.

Securing Network Infrastructure. Discover practical network security with Nmap and Nessus 7

Sairam Jetty, Sagar Rahalkar

Digitization drives technology today, which is why it’s so important for organizations to design security mechanisms for their network infrastructures. Analyzing vulnerabilities is one of the best ways to secure your network infrastructure.This Learning Path begins by introducing you to the various concepts of network security assessment, workflows, and architectures. You will learn to employ open source tools to perform both active and passive network scanning and use these results to analyze and design a threat model for network security. With a firm understanding of the basics, you will then explore how to use Nessus and Nmap to scan your network for vulnerabilities and open ports and gain back door entry into a network. As you progress through the chapters, you will gain insights into how to carry out various key scanning tasks, including firewall detection, OS detection, and access management to detect vulnerabilities in your network.By the end of this Learning Path, you will be familiar with the tools you need for network scanning and techniques for vulnerability scanning and network protection.This Learning Path includes content from the following Packt books:•Network Scanning Cookbook by Sairam Jetty•Network Vulnerability Assessment by Sagar Rahalkar

Reinforcement Learning with TensorFlow. A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

Sayon Dutta

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions.The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.

Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition

Alexander Combs, Michael Roman

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.

Django RESTful Web Services. The easiest way to build Python RESTful APIs and web services with Django

Gaston C. Hillar

Django is a Python web framework that makes the web development process very easy. It reduces the amount of trivial code, which simplifies the creation of web applications and results in faster development. It is very powerful and a great choice for creating RESTful web services.If you are a Python developer and want to efficiently create RESTful web services with Django for your apps, then this is the right book for you.The book starts off by showing you how to install and configure the environment, required software, and tools to create RESTful web services with Django and the Django REST framework. We then move on to working with advanced serialization and migrations to interact with SQLite and non-SQL data sources. We will use the features included in the Django REST framework to improve our simple web service.Further, we will create API views to process diverse HTTP requests on objects, go through relationships and hyperlinked API management, and then discover the necessary steps to include security and permissions related to data models and APIs. We will also apply throttling rules and run tests to check that versioning works as expected. Next we will run automated tests to improve code coverage. By the end of the book, you will be able to build RESTful web services with Django.

Python Interviews. Discussions with Python Experts

Michael Driscoll, Kenneth Reitz

Each of these twenty Python Interviews can inspire and refresh your relationship with Python and the people who make Python what it is today. Let these interviews spark your own creativity, and discover how you also have the ability to make your mark on a thriving tech community. This book invites you to immerse in the Python landscape, and let these remarkable programmers show you how you too can connect and share with Python programmers around the world. Learn from their opinions, enjoy their stories, and use their tech tips.• Brett Cannon - former director of the PSF, Python core developer, led the migration to Python 3.• Steve Holden - tireless Python promoter and former chairman and director of the PSF.• Carol Willing - former director of the PSF and Python core developer, Project Jupyter Steering Council member.• Nick Coghlan - founding member of the PSF's Packaging Working Group and Python core developer.• Jessica McKellar - former director of the PSF and Python activist.• Marc-André Lemburg - Python core developer and founding member of the PSF.• Glyph Lefkowitz - founder of Twisted and fellow of the PSF• Doug Hellmann - fellow of the PSF, creator of the Python Module of the Week blog, Python community member since 1998.• Massimo Di Pierro - fellow of the PSF, data scientist and the inventor of web2py. • Alex Martelli - fellow of the PSF and co-author of Python in a Nutshell.• Barry Warsaw - fellow of the PSF, Python core developer since 1995, and original member of PythonLabs.• Tarek Ziadé - founder of Afpy and author of Expert Python Programming.• Sebastian Raschka - data scientist and author of Python Machine Learning.• Wesley Chun - fellow of the PSF and author of the Core Python Programming books.• Steven Lott - Python blogger and author of Python for Secret Agents.• Oliver Schoenborn - author of Pypubsub and wxPython mailing list contributor.• Al Sweigart - bestselling author of Automate the Boring Stuff with Python and creator of the Python modules Pyperclip and PyAutoGUI.• Luciano Ramalho - fellow of the PSF and the author of Fluent Python.• Mike Bayer - fellow of the PSF, creator of open source libraries including SQLAlchemy.• Jake Vanderplas - data scientist and author of Python Data Science Handbook.

R Data Mining. Implement data mining techniques through practical use cases and real-world datasets

Andrea Cirillo

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.

Getting Started with Gulp. Automating web development workflows - Second Edition

Travis Maynard

This book is a hands-on guide to get you up to speed with gulp. You will quickly learn how to install, configure, and run your own build system. It will instill you with the ability to automate several common development tasks to vastly improve your development workflow. This book first demonstrates various Gulp use cases before running through the steps of configuring, running, and customizing Gulp, providing you with core concepts of gulp, node.js, and npm. Diving a bit deeper into the gulp ecosystem, we will discuss when and why to use a node module instead of a gulp plugin. We will also go over a few issues that we can run into while using gulp and learn about ways to work around them to improve your gulp experience.By the end of this book, you will be able to create your very own gulp build from scratch, create and maintain tasks and project builds, and automate your workflow with plugins and custom tasks.

Mastering Proxmox. Build virtualized environments using the Proxmox VE hypervisor - Third Edition

Wasim Ahmed

Proxmox is an open source server virtualization solution that has enterprise-class features for managing virtual machines, for storage, and to virtualize both Linux and Windows application workloads.You'll begin with a refresher on the advanced installation features and the Proxmox GUI to familiarize yourself with the Proxmox VE hypervisor. Then, you'll move on to explore Proxmox under the hood, focusing on storage systems, such as Ceph, used with Proxmox. Moving on, you'll learn to manage KVM virtual machines, deploy Linux containers fast, and see how networking is handled in Proxmox. You'll also learn how to protect a cluster or a VM with a firewall and explore the new high availability features introduced in Proxmox VE 5.0. Next, you'll dive deeper into the backup/restore strategy and see how to properly update and upgrade a Proxmox node. Later, you'll learn how to monitor a Proxmox cluster and all of its components using Zabbix. Finally, you'll discover how to recover Promox from disaster strikes through some real-world examples. By the end of the book, you'll be an expert at making Proxmox work in production environments with minimal downtime.

Intelligent IoT Projects in 7 Days. Build exciting projects using smart devices

Agus Kurniawan

Intelligent IoT Projects in 7 days is about creating smart IoT projects in just 7 days. This book will help you to overcome the challenge of analyzing data from physical devices. This book aims to help you put together some of the most exciting IoT projects in a short span of time. You'll be able to use these in achieving or automating everyday tasks—one project per day.We will start with a simple smart gardening system and move on to a smart parking system, and then we will make our own vending machine, a smart digital advertising dashboard, a smart speaker machine, an autonomous fire fighter robot, and finally look at a multi-robot cooperation using swarm intelligence

Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem

Ankur Ankan, Abinash Panda

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.

Spring Boot 2.0 Projects. Build production-grade reactive applications and microservices with Spring Boot

Mohamed Shazin Sadakath

Spring Boot is a lightweight framework that provides a set of tools to create production-grade applications and services. Spring Boot 2.0 Projects is a comprehensive project-based guide for those who are new to Spring, that will get you up to speed with building real-world projects. Complete with clear step-by-step instructions, these easy-to-follow tutorials demonstrate best practices and key insights into building efficient applications with Spring Boot. The book starts off by teaching you how to develop a web application using Spring Boot, followed by giving you an understanding of creating a Spring Boot-based simple blog management system that uses Elasticsearch as the data store. Next, you’ll build a RESTful web services application using Kotlin and the Spring WebFlux framework - a new framework that enables you to create reactive applications in a functional way. Toward the last few chapters, you will build a taxi-hailing API with reactive microservices using Spring Boot, in addition to developing a Twitter clone with the help of a Spring Boot backend. To build on your knowledge further, you’ll also learn how to construct an asynchronous email formatter.By the end of this book, you’ll have a firm foundation in Spring programming and understand how to build powerful, engaging applications in Java using the Spring Boot framework.

Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

James D. Miller

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.

Hands-on Design Patterns with Kotlin. Build scalable applications using traditional, reactive, and concurrent design patterns in Kotlin

Alexey Soshin

Design patterns enable you as a developer to speed up the development process by providing you with proven development paradigms. Reusing design patterns helps prevent complex issues that can cause major problems, improves your code base, promotes code reuse, and makes an architecture more robust.The mission of this book is to ease the adoption of design patterns in Kotlin and provide good practices for programmers. The book begins by showing you the practical aspects of smarter coding in Kotlin, explaining the basic Kotlin syntax and the impact of design patterns. From there, the book provides an in-depth explanation of the classical design patterns of creational, structural, and behavioral families, before heading into functional programming. It then takes you through reactive and concurrent patterns, teaching you about using streams, threads, and coroutines to write better code along the way By the end of the book, you will be able to efficiently address common problems faced while developing applications and be comfortable working on scalable and maintainable projects of any size.