Learn to design and build Virtual Reality experiences, applications, and games in Unreal Engine 4 through a series of practical, hands-on projects that teach you to create controllable avatars, user interfaces, and more. Key Features * Deploy your virtual reality applications on the latest Oculus Go and Samsung Gear * Build real-world applications such as 3D UIs, mini games, and 360° media player applications using Unreal Engine 4 * Master multiplayer networking and build rich multi-user VR experiences Book Description Unreal Engine 4 (UE4) is a powerful tool for developing VR games and applications. With its visual scripting language, Blueprint, and built-in support for all major VR headsets, it's a perfect tool for designers, artists, and engineers to realize their visions in VR. This book will guide you step-by-step through a series of projects that teach essential concepts and techniques for VR development in UE4. You will begin by learning how to think about (and design for) VR and then proceed to set up a development environment. A series of practical projects follows, taking you through essential VR concepts. Through these exercises, you'll learn how to set up UE4 projects that run effectively in VR, how to build player locomotion schemes, and how to use hand controllers to interact with the world. You'll then move on to create user interfaces in 3D space, use the editor's VR mode to build environments directly in VR, and profile/optimize worlds you've built. Finally, you'll explore more advanced topics, such as displaying stereo media in VR, networking in Unreal, and using plugins to extend the engine. Throughout, this book focuses on creating a deeper understanding of why the relevant tools and techniques work as they do, so you can use the techniques and concepts learned here as a springboard for further learning and exploration in VR. What you will learn * Understand design principles and concepts for building VR applications * Set up your development environment with Unreal Blueprints and C++ * Create a player character with several locomotion schemes * Evaluate and solve performance problems in VR to maintain high frame rates * Display mono and stereo videos in VR * Extend Unreal Engine's capabilities using various plugins Who this book is for This book is for anyone interested in learning to develop Virtual Reality games and applications using UE4. Developers new to UE4 will benefit from hands-on projects that guide readers through clearly-explained steps, while both new and experienced developers will learn crucial principles and techniques for VR development in UE4.

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Features * Understand effective synchronization strategies for faster processing using GPUs * Write parallel processing scripts with PyCuda and PyOpenCL * Learn to use the CUDA libraries like CuDNN for deep learning on GPUs Book Description GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly. What you will learn * Utilize Python libraries and frameworks for GPU acceleration * Set up a GPU-enabled programmable machine learning environment on your system with Anaconda * Deploy your machine learning system on cloud containers with illustrated examples * Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm. * Perform data mining tasks with machine learning models on GPUs * Extend your knowledge of GPU computing in scientific applications Who this book is for Data Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.

A comprehensive guide to understanding network architecture, communication protocols, and network analysis to build secure applications compatible with the latest versions of C# 8 and .NET Core 3.0 Key Features * Explore various network architectures that make distributed programming possible * Learn how to make reliable software by writing secure interactions between clients and servers * Use .NET Core for network device automation, DevOps, and software-defined networking Book Description The C# language and the .NET Core application framework provide the tools and patterns required to make the discipline of network programming as intuitive and enjoyable as any other aspect of C# programming. With the help of this book, you will discover how the C# language and the .NET Core framework make this possible. The book begins by introducing the core concepts of network programming, and what distinguishes this field of programming from other disciplines. After this, you will gain insights into concepts such as transport protocols, sockets and ports, and remote data streams, which will provide you with a holistic understanding of how network software fits into larger distributed systems. The book will also explore the intricacies of how network software is implemented in a more explicit context, by covering sockets, connection strategies such as Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), asynchronous processing, and threads. You will then be able to work through code examples for TCP servers, web APIs served over HTTP, and a Secure Shell (SSH) client. By the end of this book, you will have a good understanding of the Open Systems Interconnection (OSI) network stack, the various communication protocols for that stack, and the skills that are essential to implement those protocols using the C# programming language and the .NET Core framework. What you will learn * Understand the breadth of C#'s network programming utility classes * Utilize network-layer architecture and organizational strategies * Implement various communication and transport protocols within C# * Discover hands-on examples of distributed application development * Gain hands-on experience with asynchronous socket programming and streams * Learn how C# and the .NET Core runtime interact with a hosting network * Understand a full suite of network programming tools and features Who this book is for If you're a .NET developer or a system administrator with .NET experience and are looking to get started with network programming, then this book is for you. Basic knowledge of C# and .NET is assumed, in addition to a basic understanding of common web protocols and some high-level distributed system designs.

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key Features * Work with large amounts of agile data using distributed datasets and in-memory caching * Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3 * Employ the easy-to-use PySpark API to deploy big data Analytics for production Book Description Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively. What you will learn * Get practical big data experience while working on messy datasets * Analyze patterns with Spark SQL to improve your business intelligence * Use PySpark's interactive shell to speed up development time * Create highly concurrent Spark programs by leveraging immutability * Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation * Re-design your jobs to use reduceByKey instead of groupBy * Create robust processing pipelines by testing Apache Spark jobs Who this book is for This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.

Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key Features * Your entry point into the world of artificial intelligence using the power of Python * An example-rich guide to master various RL and DRL algorithms * Explore the power of modern Python libraries to gain confidence in building self-trained applications Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: * Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran * Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani What you will learn * Train an agent to walk using OpenAI Gym and TensorFlow * Solve multi-armed-bandit problems using various algorithms * Build intelligent agents using the DRQN algorithm to play the Doom game * Teach your agent to play Connect4 using AlphaGo Zero * Defeat Atari arcade games using the value iteration method * Discover how to deal with discrete and continuous action spaces in various environments Who this book is for If you’re an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.

Explore the different data mining techniques using the libraries and packages offered by Python Key Features * Grasp the basics of data loading, cleaning, analysis, and visualization * Use the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data mining * Your one-stop guide to build efficient data mining pipelines without going into too much theory Book Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learn * Explore the methods for summarizing datasets and visualizing/plotting data * Collect and format data for analytical work * Assign data points into groups and visualize clustering patterns * Learn how to predict continuous and categorical outputs for data * Clean, filter noise from, and reduce the dimensions of data * Serialize a data processing model using scikit-learn’s pipeline feature * Deploy the data processing model using Python’s pickle module Who this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.

Manipulate and analyze network data with the power of Python and NetworkX Key Features * Understand the terminology and basic concepts of network science * Leverage the power of Python and NetworkX to represent data as a network * Apply common techniques for working with network data of varying sizes Book Description NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems. What you will learn * Use Python and NetworkX to analyze the properties of individuals and relationships * Encode data in network nodes and edges using NetworkX * Manipulate, store, and summarize data in network nodes and edges * Visualize a network using circular, directed and shell layouts * Find out how simulating behavior on networks can give insights into real-world problems * Understand the ongoing impact of network science on society, and its ethical considerations Who this book is for If you are a programmer or data scientist who wants to manipulate and analyze network data in Python, this book is perfect for you. Although prior knowledge of network science is not necessary, some Python programming experience will help you understand the concepts covered in the book easily.

Learn best practices for testing with Jira and model industry workflows that can be used during the software development lifecycle Key Features * Integrate Jira with test management tools such as Zephyr, Test Management, and SynapseRT * Understand test case management, traceability, and test execution with reports * Implement continuous integration using Jira, Jenkins, and automated testing tools Book Description Hands-On Test Management with Jira begins by introducing you to the basic concepts of Jira and takes you through real-world software testing processes followed by various organizations. As you progress through the chapters, the book explores and compares the three most popular Jira plugins—Zephyr, Test Management, and synapseRT. With this book, you’ll gain a practical understanding of test management processes using Jira. You’ll learn how to create and manage projects, create Jira tickets to manage customer requirements, and track Jira tickets. You’ll also understand how to develop test plans, test cases, and test suites, and create defects and requirement traceability matrices, as well as generating reports in Jira. Toward the end, you’ll understand how Jira can help the SQA teams to use the DevOps pipeline for automating execution and managing test cases. You’ll get to grips with configuring Jira with Jenkins to execute automated test cases in Selenium. By the end of this book, you’ll have gained a clear understanding of how to model and implement test management processes using Jira. What you will learn * Understand QMS to effectively implement quality systems in your organization * Explore a business-driven structured approach to Test Management using TMap NEXT * Implement different aspects of test planning, test strategy, and test execution * Organize and manage Agile projects in Scrum and Kanban * Uncover Jira plugins available in the Atlassian Marketplace for testing and project management * Configure a DevOps pipeline for continuous integration using Jira with Jenkins Who this book is for If you’re a quality assurance professional, software project manager, or test manager interested in learning test management best practices in your team or organization, this book is for you. Prior knowledge of test management and Jenkins will be beneficial in understanding the concepts covered in this book.

Start empowering users and protecting corporate data, while managing identities and access with Microsoft Azure in different environments Key Features * Understand how to identify and manage business drivers during transitions * Explore Microsoft Identity and Access Management as a Service (IDaaS) solution * Over 40 playbooks to support your learning process with practical guidelines Book Description Microsoft Azure and its Identity and access management are at the heart of Microsoft's software as service products, including Office 365, Dynamics CRM, and Enterprise Mobility Management. It is crucial to master Microsoft Azure in order to be able to work with the Microsoft Cloud effectively. You’ll begin by identifying the benefits of Microsoft Azure in the field of identity and access management. Working through the functionality of identity and access management as a service, you will get a full overview of the Microsoft strategy. Understanding identity synchronization will help you to provide a well-managed identity. Project scenarios and examples will enable you to understand, troubleshoot, and develop on essential authentication protocols and publishing scenarios. Finally, you will acquire a thorough understanding of Microsoft Information protection technologies. What you will learn * Apply technical descriptions to your business needs and deployments * Manage cloud-only, simple, and complex hybrid environments * Apply correct and efficient monitoring and identity protection strategies * Design and deploy custom Identity and access management solutions * Build a complete identity and access management life cycle * Understand authentication and application publishing mechanisms * Use and understand the most crucial identity synchronization scenarios * Implement a suitable information protection strategy Who this book is for This book is a perfect companion for developers, cyber security specialists, system and security engineers, IT consultants/architects, and system administrators who are looking for perfectly up–to-date hybrid and cloud-only scenarios. You should have some understanding of security solutions, Active Directory, access privileges/rights, and authentication methods. Programming knowledge is not required but can be helpful for using PowerShell or working with APIs to customize your solutions.

Harness the power of Python objects and data structures to implement algorithms for analyzing your data and efficiently extracting information Key Features * Turn your designs into working software by learning the Python syntax * Write robust code with a solid understanding of Python data structures * Understand when to use the functional or the OOP approach Book Description This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You’ll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you’ve covered core topics, you’ll understand the joy of unit testing and just how easy it is to create unit tests. By the end of this Learning Path, you will have built components that are easy to understand, debug, and can be used across different applications. This Learning Path includes content from the following Packt products: * Learn Python Programming - Second Edition by Fabrizio Romano * Python Data Structures and Algorithms by Benjamin Baka * Python 3 Object-Oriented Programming by Dusty Phillips What you will learn * Use data structures and control flow to write code * Use functions to bundle together a sequence of instructions * Implement objects in Python by creating classes and defining methods * Design public interfaces using abstraction, encapsulation and information hiding * Raise, define, and manipulate exceptions using special error objects * Create bulletproof and reliable software by writing unit tests * Learn the common programming patterns and algorithms used in Python Who this book is for If you are relatively new to coding and want to write scripts or programs to accomplish tasks using Python, or if you are an object-oriented programmer for other languages and seeking a leg up in the world of Python, then this Learning Path is for you. Though not essential, it will help you to have basic knowledge of programming and OOP.

Build, design and improve advanced business intelligence solutions using Tableau’s latest features, including Tableau Prep, Tableau Hyper, and Tableau Server Key Features * Master new features in Tableau 2019.1 to solve real-world analytics challenges * Perform Geo-Spatial Analytics, Time Series Analysis, and self-service analytics using real-life examples * Build and publish dashboards and explore storytelling using Python and MATLAB integration support Book Description Tableau is one of the leading business intelligence (BI) tools used to solve BI and analytics challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. This book is also the second edition of the popular Mastering Tableau series, with new features, examples, and updated code. The book covers essential Tableau concepts and its advanced functionalities. Using Tableau Hyper and Tableau Prep, you’ll be able to handle and prepare data easily. You’ll gear up to perform complex joins, spatial joins, union, and data blending tasks using practical examples. Following this, you’ll learn how to perform data densification to make displaying granular data easier. Next, you’ll explore expert-level examples to help you with advanced calculations, mapping, and visual design using various Tableau extensions. With the help of examples, you’ll also learn about improving dashboard performance, connecting Tableau Server, and understanding data visualizations. In the final chapters, you’ll cover advanced use cases such as Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics, and learn to connect Tableau to R, Python, and MATLAB. By the end of this book, you’ll have mastered the advanced offerings of Tableau and be able to tackle common and not-so-common challenges faced in the BI domain. What you will learn * Get up to speed with various Tableau components * Master data preparation techniques using Tableau Prep * Discover how to use Tableau to create a PowerPoint-like presentation * Understand different Tableau visualization techniques and dashboard designs * Interact with the Tableau server to understand its architecture and functionalities * Study advanced visualizations and dashboard creation techniques * Brush up on powerful Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics Who this book is for This book is designed for business analysts, BI professionals and data analysts who want to master Tableau to solve a range of data science and business intelligence problems. The book is ideal if you have a good understanding of Tableau and want to take your skills to the next level.

Learn to create and customize impressive Drupal 8 modules to extend your website's functionalities Key Features * Explore a plethora of Drupal 8 APIs and get the best out of them using the power of PHP coding * Learn to implement efficient data management and data security by creating dedicated modules for it. * Stay up to date with the changes introduced in the new Drupal 8 releases Book Description Drupal 8 comes with a release cycle that allows for new functionality to be added at a much faster pace. However, this also means code deprecations and changing architecture that you need to stay on top of. This book updates the first edition and includes the new functionality introduced in versions up to, and including 8.7. The book will first introduce you to the Drupal 8 architecture and its subsystems before diving into creating your first module with basic functionality. You will work with the Drupal logging and mailing systems, learn how to output data using the theme layer and work with menus and links programmatically. Then, you will learn how to work with different kinds of data storages, create custom entities, field types and leverage the Database API for lower level database queries. You will further see how to introduce JavaScript into your module, work with the various file systems and ensure the code you write works on multilingual sites. Finally, you will learn how to programmatically work with Views, write automated tests for your functionality and also write secure code in general. By the end, you will have learned how to develop your own custom module that can provide complex business solutions. And who knows, maybe you’ll even contribute it back to the Drupal community. What you will learn * Develop Drupal 8 modules that do all the things you want * Master numerous Drupal 8 sub-systems and APIs in the process * Model, store, manipulate and process data to serve your purposes * Display data and content in a clean and secure way using the Drupal 8 theme system * Test your business logic to prevent regressions * Stay ahead of the curve and write code following the current best practices Who this book is for The primary target of this book is Drupal developers who want to learn how to write modules and develop in Drupal 8. It is also intended for Drupal site builders and PHP developers who have basic Object Oriented Programming skills. A little bit of Symfony experience is helpful but not mandatory.