A guide to mastering Visual Studio 2017 About This Book ? Focus on coding with the new, improved, and powerful tools of VS 2017 ? Master improved debugging and unit testing support capabilities ? Accelerate cloud development with the built-in Azure tools Who This Book Is For .NET Developers who would like to master the new features of VS 2017, and would like to delve into newer areas such as cloud computing, would benefit from this book. Basic knowledge of previous versions of Visual Studio is assumed. What You Will Learn ? Learn what's new in the Visual Studio 2017 IDE, C# 7.0, and how it will help developers to improve their productivity ? Learn the workloads and components of the new installation wizard and how to use the online and offline installer ? Build stunning Windows apps using Windows Presentation Foundation (WPF) and Universal Windows Platform (UWP) tools ? Get familiar with .NET Core and learn how to build apps targeting this new framework ? Explore everything about NuGet packages ? Debug and test your applications using Visual Studio 2017 ? Accelerate cloud development with Microsoft Azure ? Integrate Visual Studio with most popular source control repositories, such as TFS and GitHub In Detail Visual Studio 2017 is the all-new IDE released by Microsoft for developers, targeting Microsoft and other platforms to build stunning Windows and web apps. Learning how to effectively use this technology can enhance your productivity while simplifying your most common tasks, allowing you more time to focus on your project. With this book, you will learn not only what VS2017 offers, but also what it takes to put it to work for your projects. Visual Studio 2017 is packed with improvements that increase productivity, and this book will get you started with the new features introduced in Visual Studio 2017 IDE and C# 7.0. Next, you will learn to use XAML tools to build classic WPF apps, and UWP tools to build apps targeting Windows 10. Later, you will learn about .NET Core and then explore NuGet, the package manager for the Microsoft development platform. Then, you will familiarize yourself with the debugging and live unit testing techniques that comes with the IDE. Finally, you'll adapt Microsoft's implementation of cloud computing with Azure, and the Visual Studio integration with Source Control repositories. Style and approach This comprehensive guide covers the advanced features of Visual Studio 2017, and communicates them through a practical approach to explore the underlying concepts of how, when, and why to use it.

Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book ? Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more ? Analyze and extract actionable insights from your social data using various Python tools ? A highly practical guide to conducting efficient social media analytics at scale Who This Book Is For If you are a programmer or a data analyst familiar with the Python programming language and want to perform analyses of your social data to acquire valuable business insights, this book is for you. The book does not assume any prior knowledge of any data analysis tool or process. What You Will Learn ? Understand the basics of social media mining ? Use PyMongo to clean, store, and access data in MongoDB ? Understand user reactions and emotion detection on Facebook ? Perform Twitter sentiment analysis and entity recognition using Python ? Analyze video and campaign performance on YouTube ? Mine popular trends on GitHub and predict the next big technology ? Extract conversational topics on public internet forums ? Analyze user interests on Pinterest ? Perform large-scale social media analytics on the cloud In Detail Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes. Style and approach This book follows a step-by-step approach to teach readers the concepts of social media analytics using the Python programming language. To explain various data analysis processes, real-world datasets are used wherever required.

Build, Manage and Improve your infrastructure effortlessly. About This Book ? An up-to-date and comprehensive resource on Terraform that lets you quickly and efficiently launch your infrastructure ? Learn how to implement your infrastructure as code and make secure, effective changes to your infrastructure ? Learn to build multi-cloud fault-tolerant systems and simplify the management and orchestration of even the largest scale and most complex cloud infrastructures Who This Book Is For This book is for developers and operators who already have some exposure to working with infrastructure but want to improve their workflow and introduce infrastructure as a code practice. Knowledge of essential Amazon Web Services components (EC2, VPC, IAM) would help contextualize the examples provided. Basic understanding of Jenkins and Shell *s will be helpful for the chapters on the production usage of Terraform. What You Will Learn ? Understand what Infrastructure as Code (IaC) means and why it matters ? Install, configure, and deploy Terraform ? Take full control of your infrastructure in the form of code ? Manage complete infrastructure, starting with a single server and scaling beyond any limits ? Discover a great set of production-ready practices to manage infrastructure ? Set up CI/CD pipelines to test and deliver Terraform stacks ? Construct templates to simplify more complex provisioning tasks In Detail Terraform is a tool used to efficiently build, configure, and improve the production infrastructure. It can manage the existing infrastructure as well as create custom in-house solutions. This book shows you when and how to implement infrastructure as a code practices with Terraform. It covers everything necessary to set up the complete management of infrastructure with Terraform, starting with the basics of using providers and resources. It is a comprehensive guide that begins with very small infrastructure templates and takes you all the way to managing complex systems, all using concrete examples that evolve over the course of the book. The book ends with the complete workflow of managing a production infrastructure as code—this is achieved with the help of version control and continuous integration. The readers will also learn how to combine multiple providers in a single template and manage different code bases with many complex modules. It focuses on how to set up continuous integration for the infrastructure code. The readers will be able to use Terraform to build, change, and combine infrastructure safely and efficiently. Style and approach This book will help and guide you to implement Terraform in your infrastructure. The readers will start by working on very small infrastructure templates and then slowly move on to manage complex systems, all by using concrete examples that will evolve during the course of the book.

This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book ? Take your first steps in the world of data science by understanding the tools and techniques of data analysis ? Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods ? Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn ? Learn how to clean your data and ready it for analysis ? Implement the popular clustering and regression methods in Python ? Train efficient machine learning models using decision trees and random forests ? Visualize the results of your analysis using Python’s Matplotlib library ? Use Apache Spark’s MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

This book is your guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data. About This Book ? Explore various possibilities with deep learning and gain amazing insights from data using Google’s brainchild-- TensorFlow ? Want to learn what more can be done with deep learning? Explore various neural networks with the help of this comprehensive guide ? Rich in concepts, advanced guide on deep learning that will give you background to innovate in your environment Who This Book Is For If you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise in working with convoluted neural networks, then this book is for you. Some familiarity with C++ or Python is assumed. What You Will Learn ? Set up your computing environment and install TensorFlow ? Build simple TensorFlow graphs for everyday computations ? Apply logistic regression for classification with TensorFlow ? Design and train a multilayer neural network with TensorFlow ? Intuitively understand convolutional neural networks for image recognition ? Bootstrap a neural network from simple to more accurate models ? See how to use TensorFlow with other types of networks ? Program networks with SciKit-Flow, a high-level interface to TensorFlow In Detail Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond. Style and Approach This book is your go-to guide to becoming a deep learning expert in your organization. Dan helps you evaluate common and not-so-common deep neural networks with the help of insightful examples that you can relate to, and show how they can be exploited in the real world with complex raw data.

Learn everything you need to build highly scalable, robust web applications using Angular release 4 About This Book ? Apply best practices and design patterns to achieve higher scalability in your Angular applications ? Understand the latest features of Angular and create your own components ? Get acquainted with powerful, advanced techniques in Angular to build professional web applications Who This Book Is For This book is for JavaScript developers with some prior exposure to Angular, at least through basic examples. We assume that you’ve got working knowledge of HTML, CSS, and JavaScript. What You Will Learn ? Implement asynchronous programming using Angular ? Beautify your application with the UI components built to the material design specification ? Secure your web application from unauthorized users ? Create complex forms, taking full advantage of 2-way data binding ? Test your Angular applications using the Jasmine and Protractor frameworks for better efficiency ? Learn how to integrate Angular with Bootstrap to create compelling web applications ? Use Angular built-in classes to apply animation in your app In Detail Got some experience of Angular under your belt? Want to learn everything about using advanced features for developing websites? This book is everything you need for the deep understanding of Angular that will set you apart from the developer crowd. Angular has introduced a new way to build applications. Creating complex and rich web applications, with a lighter resource footprint, has never been easier or faster. Angular is now at release 4, with significant changes through previous versions. This book has been written and tested for Angular release 4. Angular is a mature technology, and you'll likely have applications built with earlier versions. This book starts by showing you best practices and approaches to migrating your existing Angular applications so that you can be immediately up-to-date. You will take an in-depth look at components and see how to control the user journey in your applications by implementing routing and navigation. You will learn how to work with asynchronous programming by using Observables. To easily build applications that look great, you will learn all about template syntax and how to beautify applications with Material Design. Mastering forms and data binding will further speed up your application development time. Learning about managing services and animations will help you to progressively enhance your applications. Next you’ll use native directives to integrate Bootstrap with Angular. You will see the best ways to test your application with the leading options such as Jasmine and Protractor. At the end of the book, you’ll learn how to apply design patterns in Angular, and see the benefits they will bring to your development. Style and approach This book provides comprehensive coverage of all aspects of development with Angular. You will learn about all the most powerful Angular concepts, with examples and best practices. This book is everything you need for the deep understanding of Angular that will set you apart from the developer crowd.

Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models. About This Book ? Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner ? Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets ? Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models. Who This Book Is For This book is indented to provide a full overview of deep learning. From the beginner in deep learning and artificial intelligence, to the data scientist who wants to become familiar with Theano and its supporting libraries, or have an extended understanding of deep neural nets. Some basic skills in Python programming and computer science will help, as well as skills in elementary algebra and calculus. What You Will Learn ? Get familiar with Theano and deep learning ? Provide examples in supervised, unsupervised, generative, or reinforcement learning. ? Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections. ? Use Theano on real-world computer vision datasets, such as for digit classification and image classification. ? Extend the use of Theano to natural language processing tasks, for chatbots or machine translation ? Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment ? Generate synthetic data that looks real with generative modeling ? Become familiar with Lasagne and Keras, two frameworks built on top of Theano In Detail This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets. Style and approach It is an easy-to-follow example book that teaches you how to perform fast, efficient computations in Python. Starting with the very basics-NumPy, installing Theano, this book will take you to the smooth journey of implementing Theano for advanced computations for machine learning and deep learning.

This unique book provides you with a wealth of tips, tricks, best practices, and answers to the day-to-day questions that programmers face in their careers. It is split into three parts: Coder Skills, Freelancer Skills, and Career Skills, providing the knowledge you need to get ahead in programming. About This Book ? Over 50 essays with practical advice on improving your programming career ? Practical focus gives solutions to common problems, and methods to become a better coder ? Includes advice for existing programmers and those wanting to begin a career in programming Who This Book Is For This book is useful for programmers of any ability or discipline. It has advice for those thinking about beginning a career in programming, those already working as a fully employed programmer, and for those working as freelance developers. What You Will Learn ? Improve your soft skills to become a better and happier coder ? Learn to be a better developer ? Grow your freelance development business ? Improve your development career ? Learn the best approaches to breaking down complex topics ? Have the confidence to charge what you're worth as a freelancer ? Succeed in developer job interviews In Detail This is an all-purpose toolkit for your programming career. It has been built by Jordan Hudgens over a lifetime of coding and teaching coding. It helps you identify the key questions and stumbling blocks that programmers encounter, and gives you the answers to them! It is a comprehensive guide containing more than 50 insights that you can use to improve your work, and to give advice in your career. The book is split up into three topic areas: Coder Skills, Freelancer Skills, and Career Skills, each containing a wealth of practical advice. Coder Skills contains advice for people starting out, or those who are already working in a programming role but want to improve their skills. It includes such subjects as: how to study and understand complex topics, and getting past skill plateaus when learning new languages. Freelancer Skills contains advice for developers working as freelancers or with freelancers. It includes such subjects as: knowing when to fire a client, and tips for taking over legacy applications. Career Skills contains advice for building a successful career as a developer. It includes such subjects as: how to improve your programming techniques, and interview guides and developer salary negotiation strategies. Style and approach This unique book provides over 50 insightful essays full of practical advice for improving your programming career. The book is split into three broad sections covering different aspects of a developer's career. Each essay is self-contained and can be read individually, or in chunks.

A practical guide to help you undertand the ability of Apache jMeter to load and performance test various server types in a more efficient way. About This Book ? Use jMeter to create and run tests to improve the performance of your webpages and applications ? Learn to build a test plan for your websites and analyze the results ? Unleash the power of various features and changes introduced in Apache jMeter 3.0 Who This Book Is For This book is for software professionals who want to understand and improve the performance of their applications with Apache jMeter. What You Will Learn ? See why performance testing is necessary and learn how to set up JMeter ? Record and test with JMeter ? Handle various form inputs in JMeter and parse results during testing ? Manage user sessions in web applications in the context of a JMeter test ? Monitor JMeter results in real time ? Perform distributed testing with JMeter ? Get acquainted with helpful tips and best practices for working with JMeter In Detail JMeter is a Java application designed to load and test performance for web application. JMeter extends to improve the functioning of various other static and dynamic resources. This book is a great starting point to learn about JMeter. It covers the new features introduced with JMeter 3 and enables you to dive deep into the new techniques needed for measuring your website performance. The book starts with the basics of performance testing and guides you through recording your first test scenario, before diving deeper into JMeter. You will also learn how to configure JMeter and browsers to help record test plans. Moving on, you will learn how to capture form submission in JMeter, dive into managing sessions with JMeter and see how to leverage some of the components provided by JMeter to handle web application HTTP sessions. You will also learn how JMeter can help monitor tests in real-time. Further, you will go in depth into distributed testing and see how to leverage the capabilities of JMeter to accomplish this. You will get acquainted with some tips and best practices with regard to performance testing. By the end of the book, you will have learned how to take full advantage of the real power behind Apache JMeter. Style and approach The book is a practical guide starting with introducing the readers to the importance of automated testing. It will then be a beginner’s journey from getting introduced to Apache jMeter to an in-detail discussion of more advanced features and possibilities with it.

Become an advanced practitioner with this progressive set of master classes on application-oriented machine learning About This Book ? Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects ? More than 15 open source Java tools in a wide range of techniques, with code and practical usage. ? More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis. Who This Book Is For This book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally, intermediate-level data analysts and data scientists with experience in Java. Preferably, you will have experience with the fundamentals of machine learning and now have a desire to explore the area further, are up to grappling with the mathematical complexities of its algorithms, and you wish to learn the complete ins and outs of practical machine learning. What You Will Learn ? Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance. ? Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining. ? Apply machine learning to real-world data with methodologies, processes, applications, and analysis. ? Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning. ? Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies. ? Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on. In Detail Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain. Style and approach A practical guide to help you explore machine learning—and an array of Java-based tools and frameworks—with the help of practical examples and real-world use cases.

Build Machine Learning models with a sound statistical understanding. About This Book ? Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. ? Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. ? Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn ? Understand the Statistical and Machine Learning fundamentals necessary to build models ? Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems ? Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages ? Analyze the results and tune the model appropriately to your own predictive goals ? Understand the concepts of required statistics for Machine Learning ? Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models ? Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Use scikit-learn to apply machine learning to real-world problems About This Book ? Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks ? Learn how to build and evaluate performance of efficient models using scikit-learn ? Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn ? Review fundamental concepts such as bias and variance ? Extract features from categorical variables, text, and images ? Predict the values of continuous variables using linear regression and K Nearest Neighbors ? Classify documents and images using logistic regression and support vector machines ? Create ensembles of estimators using bagging and boosting techniques ? Discover hidden structures in data using K-Means clustering ? Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.

Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book ? Learn Scala’s sophisticated type system that combines Functional Programming and object-oriented concepts ? Work on a wide array of applications, from simple batch jobs to stream processing and machine learning ? Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn ? Understand object-oriented & functional programming concepts of Scala ? In-depth understanding of Scala collection APIs ? Work with RDD and DataFrame to learn Spark’s core abstractions ? Analysing structured and unstructured data using SparkSQL and GraphX ? Scalable and fault-tolerant streaming application development using Spark structured streaming ? Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML ? Build clustering models to cluster a vast amount of data ? Understand tuning, debugging, and monitoring Spark applications ? Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.

Deep dive into the unified, distributed storage system in order to provide excellent performance About This Book ? Leverage Ceph's advanced features such as erasure coding, tiering, and Bluestore ? Solve large-scale problems with Ceph as a tool by understanding its strengths and weaknesses to develop the best solutions ? A practical guide that covers engaging use cases to help you use advanced features of Ceph effectively Who This Book Is For If you are a developer and an administrator who has deployed a Ceph cluster before and are curious about some of the most advanced features in order to improve performance then this book is for you What You Will Learn ?Know when and how to use some of Ceph's advanced new features ? Set up a test cluster with Ansible and some virtual machines using VirtualBox and Vagrant ?Develop novel solutions to massive problems with librados and shared object classes. ? Choose intelligent parameters for an erasure coded pool and set it up. ? Configure the Bluestore settings and see how they interact with different hardware configurations. ? Keep Ceph running through thick and thin with tuning, monitoring and disaster recovery advice. In Detail Mastering Ceph covers all that you need to know to use Ceph effectively. Starting with design goals and planning steps that should be undertaken to ensure successful deployments, you will be guided through to setting up and deploying the Ceph cluster, with the help of orchestration tools. Key areas of Ceph including Bluestore, Erasure coding and cache tiering will be covered with help of examples. Development of applications which use Librados and Distributed computations with shared object classes are also covered. A section on tuning will take you through the process of optimisizing both Ceph and its supporting infrastructure. Finally, you will learn to troubleshoot issues and handle various scenarios where Ceph is likely not to recover on its own. By the end of the book, you will be able to successfully deploy and operate a resilient high performance Ceph cluster. Style and approach A practical guide which has each chapter explaining the concept, sharing tips and tricks and a use case to implement the most powerful features of Ceph

Engineer naturally lean web pages and leverage the latest web platform features to dramatically boost page speed About This Book ? The first book for web developers that shows how to put AMP to work ? Improve your website's mobile experience and get more traffic ? Practical methods to achieve a step change in performance quickly and easily Who This Book Is For This book is for experienced web developers who are aware of the impact of slow-loading web pages on conversion rates and user engagement, and who are seeking to serve content to their end users in a rich and enticing way using the Accelerated Mobile Pages framework. You should be familiar with HTML5, CSS3, JavaScript, and JSON. What You Will Learn ? Build, validate, and deploy AMP pages ? Create interactive user notifications, navigation menus, accordions, contact pages with forms and maps ? Monetize your traffic with a variety of ad styles and providers ? Analyze your traffic by integrating analytics providers and tracking user-behavior along several dimensions ? Embed social media with amp-youtube, amp-instagram, amp-twitter, and amp-facebook ? Build e-commerce functionality including product pages and shopping carts ? Deliver rich media experiences using AMP custom elements ? Use advanced deployment techniques to extend functionality ? Install ServiceWorkers and build Progressive Web Apps for offline use In Detail Google introduced the Accelerated Mobile Pages (AMP) project to give mobile users lightning-fast response times when accessing web pages on mobile devices. AMP delivers great user experiences by providing a framework for optimizing web pages that otherwise would take much longer to load on a mobile platform. This book shows how to solve page performance issues using the mobile web technologies available today. You will learn how to build instant-loading web pages, and have them featured more prominently on Google searches. If you want your website to succeed on mobile, if you care about SEO, and if you want to stay competitive, then this book is for you! You will go on a mobile web development journey that demonstrates with concrete examples how to build lightning-fast pages that will keep your visitors on-site and happy. This journey begins by showing how to build a simple blog article-style web page using AMP. As new concepts are introduced this page is gradually refined until you will have the skills and confidence to build a variety of rich and interactive mobile web pages. These will include e-commerce product pages, interactive forms and menus, maps and commenting systems, and even Progressive Web Apps. Style and approach Get step-by-step instructions to build web pages of different types, gain expert tricks to brand your pages without bloating them, and see methods to embed advertising.

Build fast and powerful applications by harnessing the power of protocol-oriented programming in Swift 4 About This Book ? Leverage the power of protocol-oriented programming in your applications and learn from real-world use cases ? Create a flexible code base with protocols and protocol extensions ? Leverage the power of generics in Swift 4 to create very flexible frameworks Who This Book Is For This book is for Swift developers who want to learn and implement protocol-oriented programming in their real-world applications.. What You Will Learn ? Understand the differences between object-oriented programming and protocol-oriented programming ? Explore the different types that Swift offers and what pitfalls to avoid ? Delve into generics and generic programming ? Learn how to implement Copy-On-Write within your custom types ? Implement several design patterns in a protocol-oriented way ? Design applications by prioritizing the protocol first and the implementation types second In Detail Swift has become the number one language used in iOS and macOS development. The Swift standard library is developed using protocol-oriented programming techniques, generics, and first-class value semantics; therefore, every Swift developer should understand these powerful concepts and how to take advantage of them in their application design. This book will help you understand the differences between object-oriented programming and protocol-oriented programming. It will demonstrate how to work with protocol-oriented programming using real-world use cases. You will gain a solid knowledge of the various types that can be used in Swift and the differences between value and reference types. You will be taught how protocol-oriented programming techniques can be used to develop very flexible and easy-to-maintain code. By the end of the book, you will have a thorough understanding of protocol-oriented programming and how to utilize it to build powerful and practical applications. Style and approach This book is written for developers who learn best by working with code, so every concept discussed in this book is reinforced with real code examples.

Architect your .NET applications by breaking them into really small pieces—microservices—using this practical, example-based guide About This Book ? Start your microservices journey and understand a broader perspective of microservices development ? Build, deploy, and test microservices using ASP.Net MVC, Web API, and Microsoft Azure Cloud ? Get started with reactive microservices and understand the fundamentals behind it Who This Book Is For This book is for .NET Core developers who want to learn and understand microservices architecture and implement it in their .NET Core applications. It’s ideal for developers who are completely new to microservices or have just a theoretical understanding of this architectural approach and want to gain a practical perspective in order to better manage application complexity. What You Will Learn ? Compare microservices with monolithic applications and SOA ? Identify the appropriate service boundaries by mapping them to the relevant bounded contexts ? Define the service interface and implement the APIs using ASP.NET Web API ? Integrate the services via synchronous and asynchronous mechanisms ? Implement microservices security using Azure Active Directory, OpenID Connect, and OAuth 2.0 ? Understand the operations and scaling of microservices in .NET Core ? Understand the testing pyramid and implement consumer-driven contract using pact net core ? Understand what the key features of reactive microservices are and implement them using reactive extension In Detail Microservices is an architectural style that promotes the development of complex applications as a suite of small services based on business capabilities. This book will help you identify the appropriate service boundaries within the business. We'll start by looking at what microservices are, and what the main characteristics are. Moving forward, you will be introduced to real-life application scenarios, and after assessing the current issues, we will begin the journey of transforming this application by splitting it into a suite of microservices. You will identify the service boundaries, split the application into multiple microservices, and define the service contracts. You will find out how to configure, deploy, and monitor microservices, and configure scaling to allow the application to quickly adapt to increased demand in the future. With an introduction to the reactive microservices, you strategically gain further value to keep your code base simple, focusing on what is more important rather than the messy asynchronous calls. Style and approach This guide serves as a stepping stone that helps .NET Core developers in their microservices architecture. This book provides just enough theory to understand the concepts and apply the examples.

Over 80 recipes to help you improve vSphere 6.5's performance and solve problems before they arise About This Book ? The practical recipes provide cost-effective and high performance for any application running in a virtual machine ? Contains best practices and troubleshooting techniques to resolve vSphere 6.5 performance issues ? Get a comprehensive coverage of performance issues and solutions including vCenter Server design and virtual machine and application tuning Who This Book Is For If you are a system administrator and are planning to deploy vSphere 6.5 in your organization and want to maximize its performance, then this book is for you. Prior knowledge of the vSphere 6.5 concepts is essential. What You Will Learn ? Understand the VMM Scheduler, cache aware CPU Scheduler, NUMA aware CPU Scheduler, and more during the CPU Performance Design phase ? Get to know the virtual memory reclamation technique, host ballooning monitoring, and swapping activity ? Choose the right platform while designing your vCenter Server, redundant vCenter design, and vCenter SSO and its deployment ? Learn how to use various performance simulation tools ? Design VCSA Server Certificates to minimize security threats ? Use health check tools for storage and boost vSphere 6.5's performance with VAAI and VASA In Detail vSphere is a mission-critical piece of software for many businesses. It is a complex tool, and incorrect design and deployment can create performance related issues that can negatively affect the business. This book is focused on solving these problems as well as providing best practices and performance-enhancing techniques. This edition is fully updated to include all the new features in version 6.5 as well as the latest tools and techniques to keep vSphere performing at its best. This book starts with interesting recipes, such as the interaction of vSphere 6.5 components with physical layers such as CPU, memory, and networking. Then we focus on DRS, resource control design, and vSphere cluster design. Next, you'll learn about storage performance design and how it works with VMware vSphere 6.5. Moving on, you will learn about the two types of vCenter installation and the benefits of each. Lastly, the book covers performance tools that help you get the most out of your vSphere installation. By the end of this book, you will be able to identify, diagnose, and troubleshoot operational faults and critical performance issues in vSphere 6.5. Style and approach This cookbook is written in a practical, helpful style with numerous recipes focusing on answering and providing solutions to common and not-so-common performance issues and problems.

Build a variety of cross-platform applications with the world’s most complete full-stack JavaScript framework— Meteor About This Book ? Develop a set of real-world applications each exploring different features of Meteor ? Make your app more appealing by adding reactivity and responsiveness to it ? Work with the most powerful feature of Meteor—the “full stack reactivity”—through building real-time applications with many third party libraries Who This Book Is For If you are a developer who is looking forward to taking your application development skills with Meteor to next level by getting your hands-on different projects, this book is for you. What You Will Learn ? See how Meteor fits in the modern web application development by using its reactive data system ? Make your front-end behave consistently across environments by implementing a predictable state container with Redux ? Get familiar with React and overview of Angular 2 ? Add a map to your application with a real-time geolocation ? Plugin into Meteor social media APIs like Twitter’s streaming and Facebook’s Messenger ? Add search functionality from scratch to your existing app and data ? Add responsiveness with Bootstrap 4 and Google’s Material Design using Less and Sass ? Distribute your data across machines and data centers by adding Apache Cassandra to your existing stack. ? Learn how to scale your microservices with the high performant language neutral framework gRPC. ? Learn how to query multiple data sources using GraphQL. In Detail This book starts with the basic installation and overview of the main components in Meteor. You’ll get hands-on multiple versatile applications covering a wide range of topics from adding a front-end views with the hottest rendering technology React to implementing a microservices oriented architecture.All the code is written with ES6/7 which is the latest significantly improved JavaScript language. We’ll also look at real-time data streaming, server to server data exchange, responsive styles on the front-end, full-text search functionality, and integration of many third-party libraries and APIs using npm. By the end of the book, you’ll have the skills to quickly prototype and even launch your next app idea in a matter of days. Style and Approach This book takes an easy-to-follow project-based approach. Each project starts with the goal of what you will learn and an overview the technologies used.

Implement classic and functional data structures and algorithms using Python About This Book ? A step by step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures. ? Get a better understanding of advanced Python concepts such as big-o notation, dynamic programming, and functional data structures. ? Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Who This Book Is For The book will appeal to Python developers. A basic knowledge of Python is expected. What You Will Learn ? Gain a solid understanding of Python data structures. ? Build sophisticated data applications. ? Understand the common programming patterns and algorithms used in Python data science. ? Write efficient robust code. In Detail Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications. Style and approach The easy-to-read book with its fast-paced nature will improve the productivity of Python programmers and improve the performance of Python applications.

Take tiny steps to enter the big world of data science through this interesting guide About This Book ? Learn the fundamentals of machine learning and build your own intelligent applications ? Master the art of building your own machine learning systems with this example-based practical guide ? Work with important classification and regression algorithms and other machine learning techniques Who This Book Is For This book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn ? Exploit the power of Python to handle data extraction, manipulation, and exploration techniques ? Use Python to visualize data spread across multiple dimensions and extract useful features ? Dive deep into the world of analytics to predict situations correctly ? Implement machine learning classification and regression algorithms from scratch in Python ? Be amazed to see the algorithms in action ? Evaluate the performance of a machine learning model and optimize it ? Solve interesting real-world problems using machine learning and Python as the journey unfolds In Detail Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. Style and approach This book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on work—giving you a deep insight into the world of machine learning. With simple yet rich language—Python—you will understand and be able to implement the examples with ease.