Equip yourself with the most complete and comprehensive preparation experience for Identity with Windows Server 2016: Microsoft 70-742 exam. Key Features * Helps you demonstrate real-world mastery of Windows Server 2016 identity features and functionality and prepare for 70-742 * Acquire skills to reduce IT costs and deliver more business value * Enhance your existing skills through practice questions and mock tests Book Description MCSA: Windows Server 2016 certification is one of the most sought-after certifications for IT professionals, which includes working with Windows Server and performing administrative tasks around it. This book is aimed at the 70-742 certification and is part of Packt's three-book series on MCSA Windows Server 2016 certification, which covers Exam 70-740, Exam 70-741, and Exam 70-742. This exam guide covers the exam objectives for the 70-742 Identity with Windows Server 2016 exam. It starts with installing and configuring Active Directory Domain Services (AD DS), managing and maintaining AD DS objects and advanced configurations, configuring Group Policy, Active Directory Certificate Services, and Active Directory Federation Services and Rights Management. At the end of each chapter, convenient test questions will help you in preparing for the certification in a practical manner. By the end of this book, you will be able to develop the knowledge and skills needed to complete MCSA Exam 70-742: Identity with Windows Server 2016 with confidence. What you will learn * Install, configure, and maintain Active Directory Domain Services (AD DS) * Manage Active Directory Domain Services objects * Configure and manage Active Directory Certificate Services * Configure and manage Group Policy * Design, implement, and configure Active Directory Federation Services * Implement and configure Active Directory Rights Management Services Who this book is for This book primarily targets system administrators who are looking to gain knowledge about identity and access technologies with Windows Server 2016 and aiming to pass the 70-742 certification. This will also help infrastructure administrators who are looking to gain advanced knowledge and understanding of identity and access technologies with Windows Server 2016. Familiarity with the concepts such as Active Directory, DNS is assumed.

Leverage powerful features of the SQL Server and watch your infrastructure transform into a high-performing, reliable network of systems. Key Features * Explore more than 20 real-world use cases to understand SQL Server features * Get to grips with the SQL Server Always On technology * Learn how to choose HA and DR topologies for your system Book Description Professional SQL Server High Availability and Disaster Recovery explains the high availability and disaster recovery technologies available in SQL Server: Replication, AlwaysOn, and Log Shipping. You’ll learn what they are, how to monitor them, and how to troubleshoot any related problems. You will be introduced to the availability groups of AlwaysOn and learn how to configure them to extend your database mirroring. Through this book, you will be able to explore the technical implementations of high availability and disaster recovery technologies that you can use when you create a highly available infrastructure, including hybrid topologies. By the end of the book, you’ll be equipped with all that you need to know to develop robust and high performance infrastructure. What you will learn * Configure and troubleshoot Replication, AlwaysOn, and Log Shipping * Study the best practices to implement HA and DR solutions * Design HA and DR topologies for the SQL Server and study how to choose a topology for your environment * Use T-SQL to configure replication, AlwaysOn, and log shipping * Migrate from On-Premise SQL Server to Azure SQL Database * Manage and maintain AlwaysOn availability groups for extended database mirroring Who this book is for Professional SQL Server High Availability and Disaster Recovery is for you if you are a database administrator or database developer who wants to improve the performance of your production environment. Prior experience of working with SQL Server will help you get the most out of this book.

A step-by-step guide that will help you design, develop, scale, and deploy RESTful APIs with TypeScript 3 and Node.js Key Features * Gain in-depth knowledge of OpenAPI and Swagger to build scalable web services * Explore a variety of test frameworks and test runners such as Stryker, Mocha, and Chai * Create a pipeline by Dockerizing your environment using Travis CI, Google Cloud Platform, and GitHub Book Description In the world of web development, leveraging data is the key to developing comprehensive applications, and RESTful APIs help you to achieve this systematically. This book will guide you in designing and developing web services with the power of TypeScript 3 and Node.js. You'll design REST APIs using best practices for request handling, validation, authentication, and authorization. You'll also understand how to enhance the capabilities of your APIs with ODMs, databases, models and views, as well as asynchronous callbacks. This book will guide you in securing your environment by testing your services and initiating test automation with different testing approaches. Furthermore, you'll get to grips with developing secure, testable, and more efficient code, and be able to scale and deploy TypeScript 3 and Node.js-powered RESTful APIs on cloud platforms such as the Google Cloud Platform. Finally, the book will help you explore microservices and give you an overview of what GraphQL can allow you to do. By the end of this book, you will be able to use RESTful web services to create your APIs for mobile and web apps and other platforms. What you will learn * Explore various methods to plan your services in a scalable way * Understand how to handle different request types and the response status code * Get to grips with securing web services * Delve into error handling and logging your web services for improved debugging * Uncover the microservices architecture and GraphQL * Create automated CI/CD pipelines for release and deployment strategies Who this book is for If you’re a developer who has a basic understanding of REST concepts and want to learn how to design and develop RESTful APIs, this book is for you. Prior knowledge of TypeScript will help you make the most out of this book.

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features * Apply the power of deep learning to complex reasoning tasks by building a Game AI * Exploit the most recent developments in machine learning and AI for building smart games * Implement deep learning models and neural networks with Python Book Description The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning. What you will learn * Learn the foundations of neural networks and deep learning. * Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. * Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems. * Working with Unity ML-Agents toolkit and how to install, setup and run the kit. * Understand core concepts of DRL and the differences between discrete and continuous action environments. * Use several advanced forms of learning in various scenarios from developing agents to testing games. Who this book is for This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.

Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key Features * Focus on the basics of data wrangling * Study various ways to extract the most out of your data in less time * Boost your learning curve with bonus topics like random data generation and data integrity checks Book Description For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You’ll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you’ll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learn * Use and manipulate complex and simple data structures * Harness the full potential of DataFrames and numpy.array at run time * Perform web scraping with BeautifulSoup4 and html5lib * Execute advanced string search and manipulation with RegEX * Handle outliers and perform data imputation with Pandas * Use descriptive statistics and plotting techniques * Practice data wrangling and modeling using data generation techniques Who this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.

Implement neural network architectures by building them from scratch for multiple real-world applications. Key Features * From scratch, build multiple neural network architectures such as CNN, RNN, LSTM in Keras * Discover tips and tricks for designing a robust neural network to solve real-world problems * Graduate from understanding the working details of neural networks and master the art of fine-tuning them Book Description This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter. What you will learn * Build multiple advanced neural network architectures from scratch * Explore transfer learning to perform object detection and classification * Build self-driving car applications using instance and semantic segmentation * Understand data encoding for image, text and recommender systems * Implement text analysis using sequence-to-sequence learning * Leverage a combination of CNN and RNN to perform end-to-end learning * Build agents to play games using deep Q-learning Who this book is for This intermediate-level book targets beginners and intermediate-level machine learning practitioners and data scientists who have just started their journey with neural networks. This book is for those who are looking for resources to help them navigate through the various neural network architectures; you'll build multiple architectures, with concomitant case studies ordered by the complexity of the problem. A basic understanding of Python programming and a familiarity with basic machine learning are 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.

Learn how to solve real life problems using different methods like logic regression, random forests and SVM’s with TensorFlow. About This Book ? Understand predictive analytics along with its challenges and best practices ? Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. What You Will Learn ? Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration ? Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics ? Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics ? Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations ? Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. Style and approach This is a fast-paced guide that provides a quick learning solution to all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product: ? Predictive Analytics with TensorFlow by Md. Rezaul Karim

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide About This Book ? A quick reference to all important deep learning concepts and their implementations ? Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more ? Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Who This Book Is For If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required. What You Will Learn ? Solve regression and classification challenges with TensorFlow and Keras ? Learn to use Tensor Board for monitoring neural networks and its training ? Optimize hyperparameters and safe choices/best practices ? Build CNN's, RNN's, and LSTM's and using word embedding from scratch ? Build and train seq2seq models for machine translation and chat applications. ? Understanding Deep Q networks and how to use one to solve an autonomous agent problem. ? Explore Deep Q Network and address autonomous agent challenges. In Detail Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of training deep neural networks.

Implement successful private clouds with OpenStack About This Book ? Gain hands-on experience in designing a private cloud for all infrastructures ? Create a robust virtual environment for your organization ? Design, implement and deploy an OpenStack-based cloud based on the Queens release Who This Book Is For OpenStack for Architects is for Cloud architects who are responsible to design and implement a private cloud with OpenStack. System engineers and enterprise architects will also find this book useful. Basic understanding of core OpenStack services, as well as some working experience of concepts, is recommended. What You Will Learn ? Learn the overall structure of an OpenStack deployment ? Craft an OpenStack deployment process which fits within your organization ? Apply Agile Development methodologies to engineer and operate OpenStack clouds ? Build a product roadmap for Infrastructure as a Service based on OpenStack ? Make use of containers to increase the manageability and resiliency of applications running in and on OpenStack. ? Use enterprise security guidelines for your OpenStack deployment In Detail Over the past six years, hundreds of organizations have successfully implemented Infrastructure as a Service (IaaS) platforms based on OpenStack. The huge amount of investment from these organizations, including industry giants such as IBM and HP, as well as open source leaders, such as Red Hat, Canonical, and SUSE, has led analysts to label OpenStack as the most important open source technology since the Linux operating system. Due to its ambitious scope, OpenStack is a complex and fast-evolving open source project that requires a diverse skill set to design and implement it. OpenStack for Architects leads you through the major decision points that you'll face while architecting an OpenStack private cloud for your organization. This book will address the recent changes made in the latest OpenStack release i.e Queens, and will also deal with advanced concepts such as containerization, NVF, and security. At each point, the authors offer you advice based on the experience they've gained from designing and leading successful OpenStack projects in a wide range of industries. Each chapter also includes lab material that gives you a chance to install and configure the technologies used to build production-quality OpenStack clouds. Most importantly, the book focuses on ensuring that your OpenStack project meets the needs of your organization, which will guarantee a successful rollout. Style and approach This is practical, hands-on guide to implementing OpenStack clouds, where each topic is illustrated with real-world examples and then the technical points are proven in the lab. Conceptual chapters are written in discussion style to convey important concepts quickly and present decision points for choosing options.

Combine the power of Apache Spark and Python to build effective big data applications About This Book ? Perform effective data processing, machine learning, and analytics using PySpark ? Overcome challenges in developing and deploying Spark solutions using Python ? Explore recipes for efficiently combining Python and Apache Spark to process data Who This Book Is For The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book. What You Will Learn ? Configure a local instance of PySpark in a virtual environment ? Install and configure Jupyter in local and multi-node environments ? Create DataFrames from JSON and a dictionary using pyspark.sql ? Explore regression and clustering models available in the ML module ? Use DataFrames to transform data used for modeling ? Connect to PubNub and perform aggregations on streams In Detail Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications. Style and approach This book is a rich collection of recipes that will come in handy when you are working with PySpark Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.

Accelerating Business and Mission Success with Cloud Computing. About This Book ? A step-by-step guide that will practically guide you through implementing Cloud computing services effectively and efficiently. ? Learn to choose the most ideal Cloud service model, and adopt appropriate Cloud design considerations for your organization. ? Leverage Cloud computing methodologies to successfully develop a cost-effective Cloud environment successfully. Who This Book Is For If you are an IT Administrator, Cloud Architect, or a Solution Architect keen to benefit from cloud adoption for your organization, then this book is for you. Small business owners, managers, or consultants will also find this book useful. No prior knowledge of Cloud computing is needed. What You Will Learn ? Manage changes in the digital transformation and cloud transition process ? Design and build architectures that support specific business cases ? Design, modify, and aggregate baseline cloud architectures ? Familiarize yourself with cloud application security and cloud computing security threats ? Design and architect small, medium, and large cloud computing solutions In Detail Cloud adoption is a core component of digital transformation. Scaling the IT environment, making it resilient, and reducing costs are what organizations want. Architecting Cloud Computing Solutions presents and explains critical Cloud solution design considerations and technology decisions required to choose and deploy the right Cloud service and deployment models, based on your business and technology service requirements. This book starts with the fundamentals of cloud computing and its architectural concepts. It then walks you through Cloud service models (IaaS, PaaS, and SaaS), deployment models (public, private, community, and hybrid) and implementation options (Enterprise, MSP, and CSP) to explain and describe the key considerations and challenges organizations face during cloud migration. Later, this book delves into how to leverage DevOps, Cloud-Native, and Serverless architectures in your Cloud environment and presents industry best practices for scaling your Cloud environment. Finally, this book addresses (in depth) managing essential cloud technology service components such as data storage, security controls, and disaster recovery. By the end of this book, you will have mastered all the design considerations and operational trades required to adopt Cloud services, no matter which cloud service provider you choose. Style and approach This book will teach you how to architect effective and organizationally aligned Cloud computing solutions by addressing Cloud computing fundamentals, Cloud architecture considerations, Cloud technology service selection, and Cloud computing security controls.

A step-by-step guide to get you up and running with Azure Networking Services and help you build solutions that leverage effective design patterns About This Book ? Learn best practices for designing and implementing Azure Networking for Azure VMs ? Figure out the hidden secrets to designing a cost-effective environment ? Plan, design, and implement various connectivity scenarios in Azure Who This Book Is For This book is for developers, IT professionals, and database admins who have prior experience of working on Microsoft Azure and want to make the most out of Azure Networking Services. What You Will Learn ? Understand Azure networking and use the right networking service to fulfill your needs ? Design Azure Networks for Azure VMs according to best practices ? Span your environment with Azure networking solutions ? Learn to use Azure DNS ? Implement Azure Load Balancer for highly available environments ? Distribute user traffic across the world via the Azure Traffic Manager ? Control your application delivery with Azure Application Gateway In Detail Microsoft Azure networking is one of the most valuable and important offerings in Azure. No matter what solution you are building for the cloud, you'll find a compelling use for it. This book will get you up to speed quickly on Microsoft Azure Networking by teaching you how to use different networking services. By reading this book, you will develop a strong networking foundation for Azure virtual machines and for expanding your on-premise environment to Azure. Hands-On Networking with Azure starts with an introduction to Microsoft Azure networking and creating Azure Virtual Networks with subnets of different types within them. The book helps you understand the architecture of Azure networks. You will then learn the best practices for designing both Windows- and Linux-based Azure VM networks. You will also learn to expand your networks into Azure and how to use Azure DNS. Moreover, you will master best practices for dealing with Azure Load Balancer and the solutions they offer in different scenarios. Finally, we will demonstrate how the Azure Application Gateway works, offering various layer-7 load balancing capabilities for applications. By the end of this book, you will be able to architect your networking solutions for Azure. Style and approach This book provides in-depth insights into properly designing your environment and saving money on your running Azure Services. Using cutting-edge examples, you will learn to efficiently monitor, diagnose, and troubleshoot Azure Networking

Start building your very own mobile apps with this comprehensive introduction to Swift and object-oriented programming About This Book ? A complete beginner's guide to Swift programming language ? Understand core Swift programming concepts and techniques for creating popular iOS apps ? Start your journey toward building mobile app development with this practical guide Who This Book Is For This book is for beginners who are new to Swift or may have some preliminary knowledge of Objective-C. If you are interested in learning and mastering Swift in Apple’s ecosystem, namely mobile development, then this book is for you. What You Will Learn ? Become a pro at iOS development by creating simple-to-complex iOS mobile applications ? Master Playgrounds, a unique and intuitive approach to teaching Xcode ? Tackle the basics, including variables, if clauses, functions, loops and structures, classes, and inheritance ? Model real-world objects in Swift and have an in-depth understanding of the data structures used, along with OOP concepts and protocols ? Use CocoaPods, an open source Swift package manager to ease your everyday developer requirements ? Develop a wide range of apps, from a simple weather app to an Instagram-like social app ? Get ahead in the industry by learning how to use third-party libraries efficiently in your apps In Detail Swift Language is now more powerful than ever; it has introduced new ways to solve old problems and has gone on to become one of the fastest growing popular languages. It is now a de-facto choice for iOS developers and it powers most of the newly released and popular apps. This practical guide will help you to begin your journey with Swift programming through learning how to build iOS apps. You will learn all about basic variables, if clauses, functions, loops, and other core concepts; then structures, classes, and inheritance will be discussed. Next, you’ll dive into developing a weather app that consumes data from the internet and presents information to the user. The final project is more complex, involving creating an Instagram like app that integrates different external libraries. The app also uses CocoaPods as its package dependency manager, to give you a cutting-edge tool to add to your skillset. By the end of the book, you will have learned how to model real-world apps in Swift. Style and approach This book has a very practical and hands-on approach towards teaching the user the new and advanced features of Swift.

Leverage the power of Matplotlib to visualize and understand your data more effectively About This Book ? Perform effective data visualization with Matplotlib and get actionable insights from your data ? Design attractive graphs, charts, and 2D plots, and deploy them to the web ? Get the most out of Matplotlib in this practical guide with updated code and examples Who This Book Is For This book is essentially for anyone who wants to create intuitive data visualizations using the Matplotlib library. If you’re a data scientist or analyst and wish to create attractive visualizations using Python, you’ll find this book useful. Some knowledge of Python programming is all you need to get started. What You Will Learn ? Create 2D and 3D static plots such as bar charts, heat maps, and scatter plots ? Get acquainted with GTK+3, Qt5, and wxWidgets to understand the UI backend of Matplotlib ? Develop advanced static plots with third-party packages such as Pandas, GeoPandas, and Seaborn ? Create interactive plots with real-time updates ? Develop web-based, Matplotlib-powered graph visualizations with third-party packages such as Django ? Write data visualization code that is readily expandable on the cloud platform In Detail Python is a general-purpose programming language increasingly being used for data analysis and visualization. Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library. Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book. Further on, you’ll explore Matplotlib 2.1.x on the web, from a cloud-based platform using third-party packages such as Django. Finally, you will integrate interactive, real-time visualization techniques into your current workflow with the help of practical real-world examples. By the end of this book, you’ll be thoroughly comfortable with using the popular Python data visualization library Matplotlib 2.1.x and leveraging its power to build attractive, insightful, and powerful visualizations. Style and approach Step by step approach to learning the best of Matplotlib 2.1.x

A step by step guide for beginners to create interactive and dynamic websites from scratch. About This Book ? A fun-filled book with incrementing projects that would help you learn and adapt the fundamentals of web development ? Bring your web design to life with the help of HTML, CSS, JQuery, and learn to kick-start your future projects with Bootstrap ? Explore popular web development techniques such as responsive, adaptive, and material design and initiate yourself with Vue.js Who This Book Is For This book is for anyone who wants to learn about web development regardless of previous experience. It's perfect for complete beginners with zero experience; it's also great for anyone who does have some experience in a few technologies (such as HTML and CSS) but not all of them. What You Will Learn ? Understand the importance of web design and the basic design components ? Learn HTML5 and CSS3 ? Difference between adaptive and responsive web design ? Learn how to create your first website ? Add interaction and dynamic content to your website with JavaScript and JQuery ? Implement Bootstrap Framework in your project ? Get familiar with server-side rendering In Detail Web design is the process of creating websites. It encompasses several different aspects, including webpage layout, content production, and graphic design. This book offers you everything you need to know to build your websites. The book starts off by explaining the importance of web design and the basic design components used in website development. It'll show you insider tips to work quickly and efficiently with web technologies such as HTML5, CSS3, and JavaScript, concluding with a project on creating a static site with good layout. Once you've got that locked down, we'll get our hands dirty by diving straight into learning JavaScript and JQuery, ending with a project on creating dynamic content for your website. After getting our basic website up and running with the dynamic functionalities you'll move on to building your own responsive websites using more advanced techniques such as Bootstrap. Later you will learn smart ways to add dynamic content, and modern UI techniques such as Adaptive UI and Material Design. This will help you understand important concepts such as server-side rendering and UI components. Finally we take a look at various developer tools to ease your web development process. Style and approach This is a fun-filled book with conversational and engaging content ; with each incrementing project, you'll would easily learn and adapt the fundamentals of web development. Each project showcases a different use case and incrementally teaches the web development basics.

Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle About This Book ? Create and share interactive documents with live code, text and visualizations ? Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter ? Develop your widgets and interactive dashboards with these innovative recipes Who This Book Is For This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing in one tool. Prior understanding of data science concepts will be helpful, but not mandatory, to use this book. What You Will Learn ? Install Jupyter and configure engines for Python, R, Scala and more ? Access and retrieve data on Jupyter Notebooks ? Create interactive visualizations and dashboards for different scenarios ? Convert and share your dynamic codes using HTML, JavaScript, Docker, and more ? Create custom user data interactions using various Jupyter widgets ? Manage user authentication and file permissions ? Interact with Big Data to perform numerical computing and statistical modeling ? Get familiar with Jupyter's next-gen user interface - JupyterLab In Detail Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it. Style and approach The recipes in this book are highly practical and very easy to follow, and include tips and tricks that will help you crack any problem that you might come across while getting the most out of your Jupyter notebook.

Mastering material for dealing with DBA certification exams About This Book ? Prepare yourself for the IBM C2090-600 certification exam ? Cover over 50 Db2 procedures including database design, performance, and security ? Work through over 150 Q&As to gain confidence on each topic Who This Book Is For The IBM Db2 11.1 Certification Guide is an excellent choice for database administrators, architects, and application developers who are keen to obtain certification in Db2. Basic understanding of Db2 is expected in order to get the most out of this guide. What You Will Learn ? Configure and manage Db2 servers, instances, and databases ? Implement Db2 BLU Acceleration and a DB2 pureScale environment ? Create, manage, and alter Db2 database objects ? Use the partitioning capabilities available within Db2 ? Enforce constraint checking with the SET INTEGRITY command ? Utilize the Db2 problem determination (db2pd) and dsmtop tools ? Configure and manage HADR ? Understand how to encrypt data in transit and at rest In Detail IBM Db2 is a relational database management system (RDBMS) that helps you store, analyze, and retrieve data efficiently. This comprehensive book is designed to help you master all aspects of IBM Db2 database administration and prepare you to take and pass IBM's Certification Exams C2090-600. Building on years of extensive experience, the authors take you through all areas covered by the test. The book delves deep into each certification topic: Db2 server management, physical design, business rules implementation, activity monitoring, utilities, high availability, and security. IBM Db2 11.1 Certification Guide provides you with more than 150 practice questions and answers, simulating real certification examination questions. Each chapter includes an extensive set of practice questions along with carefully explained answers. This book will not just prepare you for the C2090-600 exam but also help you troubleshoot day-to-day database administration challenges. Style and approach A comprehensive certification preparation guide for the C2090-600 exam, covering all the topics in greater detail and with sample questions and answers at the end of each chapter.

Text processing and pattern matching simplified About This Book ? Master the fastest and most elegant big data munging language ? Implement text processing and pattern matching using the advanced features of AWK and GAWK ? Implement debugging and inter-process communication using GAWK Who This Book Is For This book is for developers or analysts who are inclined to learn how to do text processing and data extraction in a Unix-like environment. Basic understanding of Linux operating system and shell scripting will help you to get the most out of the book. What You Will Learn ? Create and use different expressions and control flow statements in AWK ? Use Regular Expressions with AWK for effective text-processing ? Use built-in and user-defined variables to write AWK programs ? Use redirections in AWK programs and create structured reports ? Handle non-decimal input, 2-way inter-process communication with Gawk ? Create small scripts to reformat data to match patterns and process texts In Detail AWK is one of the most primitive and powerful utilities which exists in all Unix and Unix-like distributions. It is used as a command-line utility when performing a basic text-processing operation, and as programming language when dealing with complex text-processing and mining tasks. With this book, you will have the required expertise to practice advanced AWK programming in real-life examples. The book starts off with an introduction to AWK essentials. You will then be introduced to regular expressions, AWK variables and constants, arrays and AWK functions and more. The book then delves deeper into more complex tasks, such as printing formatted output in AWK, control flow statements, GNU's implementation of AWK covering the advanced features of GNU AWK, such as network communication, debugging, and inter-process communication in the GAWK programming language which is not easily possible with AWK. By the end of this book, the reader will have worked on the practical implementation of text processing and pattern matching using AWK to perform routine tasks. Style and approach An easy-to-follow, step by step guide which will help you get to grips with real-world applications of AWK programming.

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios About This Book ? Build efficient deep learning pipelines using the popular Tensorflow framework ? Train neural networks such as ConvNets, generative models, and LSTMs ? Includes projects related to Computer Vision, stock prediction, chatbots and more Who This Book Is For This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book. What You Will Learn ? Set up the TensorFlow environment for deep learning ? Construct your own ConvNets for effective image processing ? Use LSTMs for image caption generation ? Forecast stock prediction accurately with an LSTM architecture ? Learn what semantic matching is by detecting duplicate Quora questions ? Set up an AWS instance with TensorFlow to train GANs ? Train and set up a chatbot to understand and interpret human input ? Build an AI capable of playing a video game by itself –and win it! In Detail TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. Style and approach This book contains 10 unique, end-to-end projects covering all aspects of deep learning and their implementations with TensorFlow. Each project will equip you with a unique skillset in training efficient deep learning models, and empower you to implement your own projects more confidently

Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use. About This Book ? Analyze your data using R – the most powerful statistical programming language ? Learn how to implement applied statistics using practical use-cases ? Use popular R packages to work with unstructured and structured data Who This Book Is For Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book. What You Will Learn ? Gain a thorough understanding of statistical reasoning and sampling theory ? Employ hypothesis testing to draw inferences from your data ? Learn Bayesian methods for estimating parameters ? Train regression, classification, and time series models ? Handle missing data gracefully using multiple imputation ? Identify and manage problematic data points ? Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization ? Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Data Analysis with R