Discover advanced virtualization techniques and strategies to deliver centralized desktop and application services Key Features * Leverage advanced desktop virtualization techniques and strategies to transform your organization * Build better virtualized services for your users with VMware Horizon 7.8 * Develop and deploy end-to-end virtualized solutions Book Description Desktop virtualization can be tough, but VMware Horizon 7.8 changes all that. With a rich and adaptive UX, improved security,and a range of useful features for storage and networking optimization, there's plenty to love. But to properly fall in love with it, you need to know how to use it, and that means venturing deeper into the software and taking advantage of its extensive range of features, many of which are underused and underpromoted. This guide will take you through everything you need to know to not only successfully virtualize your desktop infrastructure, but also to maintain and optimize it to keep all your users happy. We'll show you how to assess and analyze your infrastructure, and how to use that analysis to design a solution that meets your organizational and user needs. Once you've done that, you'll find out how to build your virtualized environment, before deploying your virtualized solution. But more than that,we'll also make sure you know everything you need to know about the full range of features on offer, including the mobile cloud, so that you can use them to take full control of your virtualized infrastructure. What you will learn * Successfully configure Horizon 7.8 for the needs of your users * Deliver virtual desktops, session-based desktops, and hosted applications * Become familiar with how to develop, and deploy, a complete, end-to-end solution * Discover how to optimize desktop OS images for virtual desktops * Build, optimize, and tune desktop operating systems to deliver a superior end user experience * Explore the Horizon 7.8 infrastructure so that you can take full advantage of it Who this book is for This book is ideal for system admins, and solution architects interested in gaining hands-on experience with virtualization. It will take you to an advanced level, but at a pace that ensures you are always solving real-world problems. Some experience in desktop management using Windows and Microsoft Office (and familiarity with Active Directory, SQL, Windows Remote Desktop Session Hosting, and VMware vSphere technology) is necessary.

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.

Solve real-world statistical problems using the most popular R packages and techniques Key Features * Learn how to apply statistical methods to your everyday research with handy recipes * Foster your analytical skills and interpret research across industries and business verticals * Perform t-tests, chi-squared tests, and regression analysis using modern statistical techniques Book Description 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. What you will learn * Become well versed with recipes that will help you interpret plots with R * Formulate advanced statistical models in R to understand its concepts * Perform Bayesian regression to predict models and input missing data * Use time series analysis for modelling and forecasting temporal data * Implement a range of regression techniques for efficient data modelling * Get to grips with robust statistics and hidden Markov models * Explore ANOVA (Analysis of Variance) and perform hypothesis testing Who this book is for If you are a quantitative researcher, statistician, data analyst, or data scientist looking to tackle various challenges in statistics, this book is what you need! Proficiency in R programming and basic knowledge of linear algebra is necessary to follow along the recipes covered in this book.

Efficiently deploy and manage Kubernetes clusters on a cloud Key Features * Deploy highly scalable applications with Kubernetes on Azure * Leverage AKS to deploy, manage, and operations of Kubernetes * Gain best practices from this guide to increase efficiency of container orchestration service on Cloud Book Description Microsoft is now one of the most significant contributors to Kubernetes open source projects. Kubernetes helps to create, configure, and manage a cluster of virtual machines that are preconfigured to run containerized applications. This book will be your resource for achieving successful container orchestration and deployment of Kubernetes clusters on Azure. You will learn how to deploy and manage highly scalable applications, along with how to set up a production-ready Kubernetes cluster on Azure. With this book, you will be able to reduce the complexity and operational overheads of managing a Kubernetes cluster on Azure. By the end of this book, you will not only be capable of deploying and managing Kubernetes clusters on Azure with ease, but also have the knowledge of industry best practices to work with advanced Azure Kubernetes Services (AKS) concepts for complex systems. What you will learn * Get to grips with Microsoft AKS deployment, management, and operations * Learn about the benefits of using Microsoft AKS, as well as the limitations, and avoid potential problems * Integrate Microsoft toolchains such as Visual Studio Code, and Git * Implement simple and advanced AKS solutions * Implement the automated scalability and high reliability of secure deployments with Microsoft AKS * Use kubectl commands to monitor applications Who this book is for If you’re a cloud engineer, cloud solution provider, sysadmin, site reliability engineer, or a developer interested in DevOps and are looking for an extensive guide to running Kubernetes in the Azure environment then, this book is for you. Though any previous knowledge of Kubernetes is not expected, some experience with Linux and Docker containers would be beneficial.

Optimize your marketing strategies through analytics and machine learning Key Features * Understand how data science drives successful marketing campaigns * Use machine learning for better customer engagement, retention, and product recommendations * Extract insights from your data to optimize marketing strategies and increase profitability Book Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learn * Learn how to compute and visualize marketing KPIs in Python and R * Master what drives successful marketing campaigns with data science * Use machine learning to predict customer engagement and lifetime value * Make product recommendations that customers are most likely to buy * Learn how to use A/B testing for better marketing decision making * Implement machine learning to understand different customer segments Who this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.

Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorch Key Features * Build practical, real-world AI projects on Android and iOS * Implement tasks such as recognizing handwritten digits, sentiment analysis, and more * Explore the core functions of machine learning, deep learning, and mobile vision Book Description We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users. What you will learn * Explore the concepts and fundamentals of AI, deep learning, and neural networks * Implement use cases for machine vision and natural language processing * Build an ML model to predict car damage using TensorFlow * Deploy TensorFlow on mobile to convert speech to text * Implement GAN to recognize hand-written digits * Develop end-to-end mobile applications that use AI principles * Work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch Who this book is for Mobile Artificial Intelligence Projects is for machine learning professionals, deep learning engineers, AI engineers, and software engineers who want to integrate AI technology into mobile-based platforms and applications. Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.

Build modular React web apps that are scalable, maintainable and powerful using design patterns and insightful practices Key Features * Get familiar with design patterns in React like Render props and Controlled/uncontrolled inputs * Learn about class/ functional, style and high order components with React * Work through examples that can be used to create reusable code and extensible designs Book Description React is an adaptable JavaScript library for building complex UIs from small, detached bits called components. This book is designed to take you through the most valuable design patterns in React, helping you learn how to apply design patterns and best practices in real-life situations. You’ll get started by understanding the internals of React, in addition to covering Babel 7 and Create React App 2.0, which will help you write clean and maintainable code. To build on your skills, you will focus on concepts such as class components, stateless components, and pure components. You'll learn about new React features, such as the context API and React Hooks that will enable you to build components, which will be reusable across your applications. The book will then provide insights into the techniques of styling React components and optimizing them to make applications faster and more responsive. In the concluding chapters, you’ll discover ways to write tests more effectively and learn how to contribute to React and its ecosystem. By the end of this book, you will be equipped with the skills you need to tackle any developmental setbacks when working with React. You’ll be able to make your applications more flexible, efficient, and easy to maintain, thereby giving your workflow a boost when it comes to speed, without reducing quality. What you will learn * Get familiar with the new React features,like context API and React Hooks * Learn the techniques of styling and optimizing React components * Make components communicate with each other by applying consolidate patterns * Use server-side rendering to make applications load faster * Write a comprehensive set of tests to create robust and maintainable code * Build high-performing applications by optimizing components Who this book is for This book is for web developers who want to increase their understanding of React and apply it to real-life application development. Prior experience with React and JavaScript is assumed.

Gain a complete overview of Oracle CX Cloud Suite and its tools for functions ranging from marketing to sales and commerce to service Key Features * Make optimal use of your Oracle CX Cloud Suite to improve business results * Achieve improved customer insights through Oracle CX’s advanced capabilities * Learn how to design a CX solution architecture Book Description Oracle CX Cloud offers features and capabilities that help companies excel at sales, customer management, and much more. This book is a detailed guide to implementing cloud solutions and helping administrators of all levels thoroughly understand the platform. Oracle CX Cloud Suite begins with an introduction to high-level Oracle architecture and examines what CX offers over CRM. You’ll explore the different cloud-based tools for marketing, sales, and customer services, among others. The book then delves into deployment by covering basic settings, setting up users, and provisioning. You’ll see how to integrate the CX suite to work together to interact with the environment and connect with legacy systems, social connectors, and internet services. The book concludes with a use case demonstrating how the entire Oracle CX Suite is set up, and also covers how to leverage Oracle ICS and Oracle CX Cloud for hybrid deployment. By end of the book, you will have learned about the working of the Oracle CX Cloud Suite and how to orchestrate user experience across all products seamlessly. What you will learn * Differentiate between Oracle CRM and CX Cloud suites * Explore a variety of Oracle CX Cloud tools for marketing and sales * Set up users and database connections during deployment * Employ Cloud Suite CX tools to aid in planning and analysis * Implement hybrid Oracle CX solutions and connect with legacy systems * Integrate with social media connectors like Facebook and LinkedIn * Leverage Oracle ICS and Oracle CX Suite to improve business results Who this book is for This book is for administrators who want to develop and strengthen their Oracle CX Cloud Suite skills in the areas of configuration and system management. Whether you are a new administrator or an experienced professional, this book will enhance your understanding of the new Oracle CX features.

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.

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features * Learn techniques to use data to identify the exact problem to be solved * Visualize data using different graphs * Identify how to select an appropriate algorithm for data extraction Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learn * Install the required packages to set up a data science coding environment * Load data into a Jupyter Notebook running Python * Use Matplotlib to create data visualizations * Fit a model using scikit-learn * Use lasso and ridge regression to reduce overfitting * Fit and tune a random forest model and compare performance with logistic regression * Create visuals using the output of the Jupyter Notebook Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.

Concepts, tools, and techniques to explore deep learning architectures and methodologies Key Features * Explore advanced deep learning architectures using various datasets and frameworks * Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more * Discover design patterns and different challenges for various deep learning architectures Book Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learn * Implement CNNs, RNNs, and other commonly used architectures with Python * Explore architectures such as VGGNet, AlexNet, and GoogLeNet * Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more * Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples * Master artificial intelligence and neural network concepts and apply them to your architecture * Understand deep learning architectures for mobile and embedded systems Who this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

Explore the entire Hyperledger blockchain family, including frameworks such as Fabric, Sawtooth, Indy, Burrow, and Iroha; and tools such as Composer, Explorer, and Caliper. Key Features * Plan, design, and create a full-fledged private decentralized application using Hyperledger services * Master the ins and outs of the Hyperledger network using real-world examples * Packed with problem-solution-based recipes to tackle pain areas in the blockchain development cycle Book Description Hyperledger is an open-source project and creates private blockchain applications for a range of domains. This book will be your desk reference as you explore common and not-so-common challenges faced while building blockchain networks using Hyperledger services. We'll work through all Hyperledger platform modules to understand their services and features and build end-to-end blockchain applications using various frameworks and tools supported by Hyperledger. This book's independent, recipe-based approach (packed with real-world examples) will familiarize you with the blockchain development cycle. From modeling a business network to integrating with various tools, you will cover it all. We'll cover common and not-so-common challenges faced in the blockchain life cycle. Later, we'll delve into how we can interact with the Hyperledger Fabric blockchain, covering all the principles you need to master, such as chaincode, smart contracts, and much more. We'll also address the scalability and security issues currently faced in blockchain development. By the end of this book, you will be able to implement each recipe to plan, design, and create a full-fledged, private, decentralized application to meet organizational needs. What you will learn * Create the most popular permissioned blockchain network with Fabric and Composer * Build permissioned and permission-less blockchains using Sawtooth * Utilize built-in Iroha asset/account management with role-based permissions * Implement and run Ethereum smart contracts with Burrow * Get to grips with security and scalability in Hyperledger * Explore and view blockchain data using Hyperledger Explorer * Produce reports containing performance indicators and benchmarks using Caliper Who this book is for This book is for blockchain developers who want to understand how they can apply Hyperledger services in their day-to-day projects. This book uses a recipe-based approach to help you use Hyperledger to build powerful, decentralized autonomous applications. We assume the reader has a basic knowledge of the Blockchain technology and cryptography concepts

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.

Design, deploy, and manage large-scale containers using Kubernetes Key Features * Gain insight into the latest features of Kubernetes, including Prometheus and API aggregation * Discover ways to keep your clusters always available, scalable, and up-to-date * Master the skills of designing and deploying large clusters on various cloud platforms Book Description If you are running a number of containers and want to be able to automate the way they’re managed, it can be helpful to have Kubernetes at your disposal. This Learning Path guides you through core Kubernetes constructs, such as pods, services, replica sets, replication controllers, and labels. You'll get started by learning how to integrate your build pipeline and deployments in a Kubernetes cluster. As you cover more chapters in the Learning Path, you'll get up to speed with orchestrating updates behind the scenes, avoiding downtime on your cluster, and dealing with underlying cloud provider instability in your cluster. With the help of real-world use cases, you'll also explore options for network configuration, and understand how to set up, operate, and troubleshoot various Kubernetes networking plugins. In addition to this, you'll gain insights into custom resource development and utilization in automation and maintenance workflows. By the end of this Learning Path, you'll have the expertise you need to progress from an intermediate to an advanced level of understanding Kubernetes. This Learning Path includes content from the following Packt products: * Getting Started with Kubernetes - Third Edition by Jonathan Baier and Jesse White * Mastering Kubernetes - Second Edition by Gigi Sayfan What you will learn * Download, install, and configure the Kubernetes code base * Create and configure custom Kubernetes resources * Use third-party resources in your automation workflows * Deliver applications as standard packages * Set up and access monitoring and logging for Kubernetes clusters * Set up external access to applications running in the cluster * Manage and scale Kubernetes with hosted platforms on Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP) * Run multiple clusters and manage them from a single control plane Who this book is for If you are a developer or a system administrator with an intermediate understanding of Kubernetes and want to master its advanced features, then this book is for you. Basic knowledge of networking is required to easily understand the concepts explained.

Learn how you can control LEDs, make music, and read sensor data using popular microcontrollers such as Adafruit Circuit Playground, ESP8266, and the BBC micro:bit Key Features * Load and execute your first program with MicroPython * Program an IoT device to retrieve weather data using a RESTful API * Get to grips with integrating hardware, programming, and networking concepts with MicroPython Book Description MicroPython is an open source implementation of Python 3 that runs in embedded environments. With MicroPython, you can write clean and simple Python code to control hardware instead of using complex low-level languages like C and C++. This book guides you through all the major applications of the MicroPython platform to build and program projects that use microcontrollers. The MicroPython book covers recipes that’ll help you experiment with the programming environment and hardware programmed in MicroPython. You’ll find tips and techniques for building a variety of objects and prototypes that can sense and respond to touch, sound, position, heat, and light. This book will take you through the uses of MicroPython with a variety of popular input devices and sensors. You’ll learn techniques for handling time delays and sensor readings, and apply advanced coding techniques to create complex projects. As you advance, you’ll get to deal with Internet of Things (IoT) devices and integration with other online web services. Furthermore, you'll also use MicroPython to make music with bananas and create portable multiplayer video games that incorporate sound and light animations into the game play. By the end of the book, you'll have mastered tips and tricks to troubleshoot your development problems and push your MicroPython project to the next level! What you will learn * Execute code without any need for compiling or uploading using REPL (read-evaluate-print-loop) * Program and control LED matrix and NeoPixel drivers to display patterns and colors * Build projects that make use of light, temperature, and touch sensors * Configure devices to create Wi-Fi access points and use network modules to scan and connect to existing networks * Use Pulse Width Modulation to control DC motors and servos * Build an IoT device to display live weather data from the Internet at the touch of a button Who this book is for If you want to build and program projects that use microcontrollers, this book will offer you dozens of recipes to guide you through all the major applications of the MicroPython platform. Although no knowledge of MicroPython or microcontrollers is expected, a general understanding of Python is necessary to get started with this book.

An advanced guide to creating powerful high-performance GUIs for modern, media-rich applications in various domains such as business and game development Key Features * Gain comprehensive knowledge of Python GUI development using PyQt 5.12 * Explore advanced topics including multithreaded programming, 3D animation, and SQL databases * Build cross-platform GUIs for Windows, macOS, Linux, and Raspberry Pi Book Description PyQt5 has long been the most powerful and comprehensive GUI framework available for Python, yet there is a lack of cohesive resources available to teach Python programmers how to use it. This book aims to remedy the problem by providing comprehensive coverage of GUI development with PyQt5. You will get started with an introduction to PyQt5, before going on to develop stunning GUIs with modern features. You will then learn how to build forms using QWidgets and learn about important aspects of GUI development such as layouts, size policies, and event-driven programming. Moving ahead, you’ll discover PyQt5’s most powerful features through chapters on audio-visual programming with QtMultimedia, database-driven software with QtSQL, and web browsing with QtWebEngine. Next, in-depth coverage of multithreading and asynchronous programming will help you run tasks asynchronously and build high-concurrency processes with ease. In later chapters, you’ll gain insights into QOpenGLWidget, along with mastering techniques for creating 2D graphics with QPainter. You’ll also explore PyQt on a Raspberry Pi and interface it with remote systems using QtNetwork. Finally, you will learn how to distribute your applications using setuptools and PyInstaller. By the end of this book, you will have the skills you need to develop robust GUI applications using PyQt. What you will learn * Get to grips with the inner workings of PyQt5 * Learn how elements in a GUI application communicate with signals and slots * Learn techniques for styling an application * Explore database-driven applications with the QtSQL module * Create 2D graphics with QPainter * Delve into 3D graphics with QOpenGLWidget * Build network and web-aware applications with QtNetwork and QtWebEngine Who this book is for This book is for programmers who want to create attractive, functional, and powerful GUIs using the Python language. You’ll also find this book useful if you are a student, professional, or anyone who wants to start exploring GUIs or take your skills to the next level. Although prior knowledge of the Python language is assumed, experience with PyQt, Qt, or GUI programming is not required.

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features *Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation *Build deep learning models for object detection, image classification, similarity learning, and more *Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: *Mastering Machine Learning Algorithms by Giuseppe Bonaccorso *Mastering TensorFlow 1.x by Armando Fandango *Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn *Explore how an ML model can be trained, optimized, and evaluated *Work with Autoencoders and Generative Adversarial Networks *Explore the most important Reinforcement Learning techniques *Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.