Do you want to make sense of your data? Do you want to create interactive charts, data trees, info-graphics, geospatial charts, and maps efficiently? This book is your ideal choice to master interactive data visualization with D3.js V4. The book includes a number of extensive examples that to help you hone your skills with data visualization. Throughout nine chapters these examples will help you acquire a clear practical understanding of the various techniques, tools and functionality provided by D3.js. You will first setup your D3.JS development environment and learn the basic patterns needed to visualize your data. After that you will learn techniques to optimize different processes such as working with selections; animating data transitions; creating graps and charts, integrating external resources (static as well as streaming); visualizing information on maps; working with colors and scales; utilizing the different D3.js APIs; and much more. The book will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. The extensive examples will include working with complex and realtime data streams, such as seismic data, geospatial data, scientific data, and more. Towards the end of the book, you will learn to add more functionality on top of D3.js by using it with other external libraries and integrating it with Ecma* 6 and Type*

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book ?Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow ?Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide ?Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn ?Learn about machine learning landscapes along with the historical development and progress of deep learning ?Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x ?Access public datasets and utilize them using TensorFlow to load, process, and transform data ?Use TensorFlow on real-world datasets, including images, text, and more ?Learn how to evaluate the performance of your deep learning models ?Using deep learning for scalable object detection and mobile computing ?Train machines quickly to learn from data by exploring reinforcement learning techniques ?Explore active areas of deep learning research and applications

Writing concurrent and parallel programming applications is an integral skill for any Java programmer. Java 9 comes with a host of fantastic features, including significant performance improvements and new APIs. This book will take you through all the new APIs, showing you how to build parallel and multi-threaded applications. The book covers all the elements of the Java Concurrency API, with essential recipes that will help you take advantage of the exciting new capabilities. You will learn how to use parallel and reactive streams to process massive data sets. Next, you will move on to create streams and use all their intermediate and terminal operations to process big collections of data in a parallel and functional way. Further, you ll discover a whole range of recipes for almost everything, such as thread management, synchronization, executors, parallel and reactive streams, and many more. At the end of the book, you will learn how to obtain information about the status of some of the most useful components of the Java Concurrency API and how to test concurrent applications using different tools. What you will learn ?Find out to manage the basic components of the Java Concurrency API ?Use synchronization mechanisms to avoid data race conditions and other problems of concurrent applications ?Separate the thread management from the rest of the application with the Executor framework ?Solve problems using a parallelized version of the divide and conquer paradigm with the Fork / Join framework ?Process massive data sets in an optimized way using streams and reactive streams ?See which data structures we can use in concurrent applications and how to use them ?Practice efficient techniques to test concurrent applications ?Get to know tips and tricks to design concurrent applications

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. What you will learn ?Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm ?Fine-tune a neural network to improve the quality of results ?Use deep learning for image and audio processing ?Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases ?Identify problems

Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis. The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data. What you will learn ?Get to know how to measure geographic distributions ?Perform clustering analysis including hot spot and outlier analysis ?Conduct data conversion tasks using the Utilities toolset ?Understand how to use the tools provided by the Mapping Clusters toolset in the Spatial Statistics Toolbox ?Get to grips with the basics of R for performing spatial statistical programming ?Create custom ArcGIS tools with R and ArcGIS Bridge ?Understand the application of Spatial Statistics tools

Over 90 practical, actionable recipes to automate, test, and manage your infrastructure quickly and effectively About This Book Bring down your delivery timeline from days to hours by treating your server configurations and VMs as code, just like you would with software code. Take your existing knowledge and skill set with your existing tools (Puppet, Chef, or Docker) to the next level and solve IT infrastructure challenges. Use practical recipes to use code to provision and deploy servers and applications and have greater control of your infrastructure. Who This Book Is For This book is for DevOps engineers and developers working in cross-functional teams or operations and would now switch to IAC to manage complex infrastructures. What You Will Learn Provision local and remote development environments with Vagrant Automate production infrastructures with Terraform, Ansible and Cloud-init on AWS, OpenStack, Google Cloud, Digital Ocean, and more Manage and test automated systems using Chef and Puppet Build, ship, and debug optimized Docker containers Explore the best practices to automate and test everything from cloud infrastructures to operating system configuration In Detail Infrastructure as Code (IAC) is a key aspect of the DevOps movement, and this book will show you how to transform the way you work with your infrastructure—by treating it as software. This book is dedicated to helping you discover the essentials of infrastructure automation and its related practices; the over 90 organized practical solutions will demonstrate how to work with some of the very best tools and cloud solutions. You will learn how to deploy repeatable infrastructures and services on AWS, OpenStack, Google Cloud, and Digital Ocean. You will see both Ansible and Terraform in action, manipulate the best bits from cloud-init to easily bootstrap instances, and simulate consistent environments locally or remotely using Vagrant. You will discover how to automate and test a range of system tasks using Chef or Puppet. You will also build, test, and debug various Docker containers having developers’ interests in mind. This book will help you to use the right tools, techniques, and approaches to deliver working solutions for today’s modern infrastructure challenges. Style and approach This is a recipe-based book that allows you to venture into some of the most cutting-edge practices and techniques about IAC and solve immediate problems when trying to implement them.

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings. What you will learn ?Get a solid understanding of the data processing toolbox available in Java ?Explore the data science ecosystem available in Java

Work through recipes to unlock the full potential of the next generation graphics API―Vulkan About This Book ?This book explores a wide range of modern graphics programming techniques and GPU compute methods to make the best use of the Vulkan API ?Learn techniques that can be applied to a wide range of platforms desktop, smartphones, and embedded devices Who This Book Is For This book is ideal for developers who know C/C++ languages, have some basic familiarity with graphics programming, and now want to take advantage of the new Vulkan API in the process of building next generation computer graphics. Some basic familiarity of Vulkan would be useful to follow the recipes. OpenGL developers who want to take advantage of the Vulkan API will also find this book useful. What You Will Learn?Work with Swapchain to present images on screen ?Create, submit, and synchronize operations processed by the hardware ?Create buffers and images, manage their memory, and upload data to them from CPU ?Explore de*or sets and set up an interface between application and shaders ?Organize drawing operations into a set of render passes and subpasses ?Prepare graphics pipelines to draw 3D scenes and compute pipelines to perform mathematical calculations ?Implement geometry projection and tessellation, texturing, lighting, and post-processing techniques ?Write shaders in GLSL and convert them into SPIR-V assemblies ?Find out about and

"Key Features ?Get to the grips with the latest version of Apache Spark ?Utilize Spark's machine learning library to implement predictive analytics ?Leverage Spark's powerful tools to load, analyze, clean, and transform your data Book De*ion Spark ML is the machine learning module of Spark. It uses in-memory RDDs to process machine learning models faster for clustering, classification, and regression. This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. What you will learn ?Get hands-on with the latest version of Spark ML ?Create your first Spark program with Scala and Python ?Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2 ?Access public machine learning datasets and use Spark to load, process, clean, and transform data ?Use Spark's machine learning library to implement programs by utilizing well-known machine learning models ?Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models ?Write Spark functions to evaluate the performance of your machine learning models "

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book ? Load, store, edit, and visualize data using OpenCV and Python ? Grasp the fundamental concepts of classification, regression, and clustering ? Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide ? Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn ? Explore and make effective use of OpenCV's machine learning module ? Learn deep learning for computer vision with Python ? Master linear regression and regularization techniques ? Classify objects such as flower species, handwritten digits, and pedestrians ? Explore the effective use of support vector machines, boosted decision trees, and random forests ? Get acquainted with neural networks and Deep Learning to address real-world problems ? Discover hidden structures in your data using k-means clustering ? Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch! Style and approach OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.

Master the principles to make applications robust, scalable and responsive About This Book ? Implement concurrent applications using the Java 9 Concurrency API and its new components ? Improve the performance of your applications and process more data at the same time, taking advantage of all of your resources ? Construct real-world examples related to machine learning, data mining, natural language processing, and more Who This Book Is For This book is for competent Java developers who have basic understanding of concurrency, but knowledge of effective implementation of concurrent programs or usage of streams for making processes more efficient is not required What You Will Learn ? Master the principles that every concurrent application must follow ? See how to parallelize a sequential algorithm to obtain better performance without data inconsistencies and deadlocks ? Get the most from the Java Concurrency API components ? Separate the thread management from the rest of the application with the Executor component ? Execute phased-based tasks in an efficient way with the Phaser components ? Solve problems using a parallelized version of the divide and conquer paradigm with the Fork / Join framework ? Find out how to use parallel Streams and Reactive Streams ? Implement the “map and reduce” and “map and collect” programming models ? Control the concurrent data structures and synchronization mechanisms provided by the Java Concurrency API ? Implement efficient solutions for some actual problems such as data mining, machine learning, and more In Detail Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs. The book starts with a full de*ion of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications. The book ends with a detailed de*ion of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM. Style and approach This is a complete guide that implements real-world examples of algorithms related to machine learning, data mining, and natural language processing in client/server environments. All the examples are explained using a step-by-step approach.

Over 60 recipes to install, configure, and manage your IIS 10.0 About This Book ? Provide a secure, easy-to-manage extensible platform for hosting your websites ? Leverage IIS 10.0 in order to deploy web site in seconds ? Integrate Windows and Nano Server 2016 and automate it with PowerShell ? Recipes to Manage and monitor your IIS 10.0 Who This Book Is For If you are an administrator or web developer with a basic (or no) knowledge of Microsoft IIS and want to set up your own web server, then this is the book for you. What You Will Learn ? Integrate IIS 10.0 on Windows server 2016 ? Host multiple websites and Wildcard Host on IIS 10.0 ? Deploy and administrate IIS 10.0 on Nano Server. ? IIS administration with Powershell. ? Manage and troubleshoot IIS 10.0 In Detail This book will start with customizing your IIS 10 to various platforms/OS and tune it according to your business requirements. Moving on, we will focus on the functionalities of core fundamentals and perform practical scenarios in order to maximize the use of a reliable web server. Going further we will be covering topics like IIS 10 architecture, IIS modules,hosting web server platforms, virtual directories along with web site deployment, ports, enhanced security. We will also cover new features of IIS 10 like integration with Windows Server 2016 and Nano Server, HTTP/2, PowerShell 5 cmdlets etc . Towards the end, we will cover troubleshooting & diagnostic techniques of IIS 10. By the end of this book you will be well versed with maximizing the reliability of your webserver and will have immense knowledge in using IIS 10 effectively Style and approach A set of exciting recipes on using Microsoft IIS 10.0 effectively..

Develop cloud native applications with microservices using Spring Boot, Spring Cloud, and Spring Cloud Data Flow About This Book ? Explore the new features and components in Spring ? Evolve towards micro services and cloud native applications ? Gain powerful insights into advanced concepts of Spring and Spring Boot to develop applications more effectively ? Understand the basics of Kotlin and use it to develop a quick service with Spring Boot Who This Book Is For This book is for an experienced Java developer who knows the basics of Spring, and wants to learn how to use Spring Boot to build applications and deploy them to the cloud. What You Will Learn ? Explore the new features in Spring Framework 5.0 ? Build microservices with Spring Boot ? Get to know the advanced features of Spring Boot in order to effectively develop and monitor applications ? Use Spring Cloud to deploy and manage applications on the Cloud ? Understand Spring Data and Spring Cloud Data Flow ? Understand the basics of reactive programming ? Get to know the best practices when developing applications with the Spring Framework ? Create a new project using Kotlin and implement a couple of basic services with unit and integration testing In Detail Spring 5.0 is due to arrive with a myriad of new and exciting features that will change the way we’ve used the framework so far. This book will show you this evolution—from solving the problems of testable applications to building distributed applications on the cloud. The book begins with an insight into the new features in Spring 5.0 and shows you how to build an application using Spring MVC. You will realize how application architectures have evolved from monoliths to those built around microservices. You will then get a thorough understanding of how to build and extend microservices using Spring Boot. You will also understand how to build and deploy Cloud-Native microservices with Spring Cloud. The advanced features of Spring Boot will be illustrated through powerful examples. We will be introduced to a JVM language that’s quickly gaining popularity - Kotlin. Also, we will discuss how to set up a Kotlin project in Eclipse. By the end of the book, you will be equipped with the knowledge and best practices required to develop microservices with the Spring Framework. Style and Approach This book follows an end-to-end tutorial approach with lots of examples and sample applications, covering the major building blocks of the Spring framework.

Become an expert in implementing advanced, network-related tasks with Python. About This Book ? Build the skills to perform all networking tasks using Python with ease ? Use Python for network device automation, DevOps, and software-defined networking ? Get practical guidance to networking with Python Who This Book Is For If you are a network engineer or a programmer who wants to use Python for networking, then this book is for you. A basic familiarity with networking-related concepts such as TCP/IP and a familiarity with Python programming will be useful. What You Will Learn ? Review all the fundamentals of Python and the TCP/IP suite ? Use Python to execute commands when the device does not support the API or programmatic interaction with the device ? Implement automation techniques by integrating Python with Cisco, Juniper, and Arista eAPI ? Integrate Ansible using Python to control Cisco, Juniper, and Arista networks ? Achieve network security with Python ? Build Flask-based web-service APIs with Python ? Construct a Python-based migration plan from a legacy to scalable SDN-based network. In Detail This book begins with a review of the TCP/ IP protocol suite and a refresher of the core elements of the Python language. Next, you will start using Python and supported libraries to automate network tasks from the current major network vendors. We will look at automating traditional network devices based on the command-line interface, as well as newer devices with API support, with hands-on labs. We will then learn the concepts and practical use cases of the Ansible framework in order to achieve your network goals. We will then move on to using Python for DevOps, starting with using open source tools to test, secure, and analyze your network. Then, we will focus on network monitoring and visualization. We will learn how to retrieve network information using a polling mechanism, ?ow-based monitoring, and visualizing the data programmatically. Next, we will learn how to use the Python framework to build your own customized network web services. In the last module, you will use Python for SDN, where you will use a Python-based controller with OpenFlow in a hands-on lab to learn its concepts and applications. We will compare and contrast OpenFlow, OpenStack, OpenDaylight, and NFV. Finally, you will use everything you’ve learned in the book to construct a migration plan to go from a legacy to a scalable SDN-based network. Style and approach An easy-to-follow guide packed with hands-on examples of using Python for network device automation, DevOps, and SDN.

Over 90 recipes that leverage the powerful features of the Standard Library in C++17 About This Book ? Learn the latest features of C++ and how to write better code by using the Standard Library (STL). Reduce the development time for your applications. ? Understand the scope and power of STL features to deal with real-world problems. ? Compose your own algorithms without forfeiting the simplicity and elegance of the STL way. Who This Book Is For This book is for intermediate-to-advanced C++ programmers who want to get the most out of the Standard Template Library of the newest version of C++: C++ 17. What You Will Learn ? Learn about the new core language features and the problems they were intended to solve ? Understand the inner workings and requirements of iterators by implementing them ? Explore algorithms, functional programming style, and lambda expressions ? Leverage the rich, portable, fast, and well-tested set of well-designed algorithms provided in the STL ? Work with strings the STL way instead of handcrafting C-style code ? Understand standard support classes for concurrency and synchronization, and how to put them to work ? Use the filesystem library addition available with the C++17 STL In Detail C++ has come a long way and is in use in every area of the industry. Fast, efficient, and flexible, it is used to solve many problems. The upcoming version of C++ will see programmers change the way they code. If you want to grasp the practical usefulness of the C++17 STL in order to write smarter, fully portable code, then this book is for you. Beginning with new language features, this book will help you understand the language’s mechanics and library features, and offers insight into how they work. Unlike other books, ours takes an implementation-specific, problem-solution approach that will help you quickly overcome hurdles. You will learn the core STL concepts, such as containers, algorithms, utility classes, lambda expressions, iterators, and more, while working on practical real-world recipes. These recipes will help you get the most from the STL and show you how to program in a better way. By the end of the book, you will be up to date with the latest C++17 features and save time and effort while solving tasks elegantly using the STL. Style and approach This recipe-based guide will show you how to make the best use of C++ together with the STL to squeeze more out of the standard language

Learn and implement various Quantitative Finance concepts using the popular Python libraries About This Book ? Understand the fundamentals of Python data structures and work with time-series data ? Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib ? A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance Who This Book Is For This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data. What You Will Learn ? Become acquainted with Python in the first two chapters ? Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models ? Learn how to price a call, put, and several exotic options ? Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options ? Understand the concept of volatility and how to test the hypothesis that volatility changes over the years ? Understand the ARCH and GARCH processes and how to write related Python programs In Detail This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option. Style and approach This book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding.

Over 100 practical recipes to help you become an expert Hadoop administrator About This Book ? Become an expert Hadoop administrator and perform tasks to optimize your Hadoop Cluster ? Import and export data into Hive and use Oozie to manage workflow. ? Practical recipes will help you plan and secure your Hadoop cluster, and make it highly available Who This Book Is For If you are a system administrator with a basic understanding of Hadoop and you want to get into Hadoop administration, this book is for you. It’s also ideal if you are a Hadoop administrator who wants a quick reference guide to all the Hadoop administration-related tasks and solutions to commonly occurring problems What You Will Learn ? Set up the Hadoop architecture to run a Hadoop cluster smoothly ? Maintain a Hadoop cluster on HDFS, YARN, and MapReduce ? Understand high availability with Zookeeper and Journal Node ? Configure Flume for data ingestion and Oozie to run various workflows ? Tune the Hadoop cluster for optimal performance ? Schedule jobs on a Hadoop cluster using the Fair and Capacity scheduler ? Secure your cluster and troubleshoot it for various common pain points In Detail Hadoop enables the distributed storage and processing of large datasets across clusters of computers. Learning how to administer Hadoop is crucial to exploit its unique features. With this book, you will be able to overcome common problems encountered in Hadoop administration. The book begins with laying the foundation by showing you the steps needed to set up a Hadoop cluster and its various nodes. You will get a better understanding of how to maintain Hadoop cluster, especially on the HDFS layer and using YARN and MapReduce. Further on, you will explore durability and high availability of a Hadoop cluster. You’ll get a better understanding of the schedulers in Hadoop and how to configure and use them for your tasks. You will also get hands-on experience with the backup and recovery options and the performance tuning aspects of Hadoop. Finally, you will get a better understanding of troubleshooting, diagnostics, and best practices in Hadoop administration. By the end of this book, you will have a proper understanding of working with Hadoop clusters and will also be able to secure, encrypt it, and configure auditing for your Hadoop clusters. Style and approach This book contains short recipes that will help you run a Hadoop cluster efficiently. The recipes are solutions to real-life problems that administrators encounter while working with a Hadoop cluster

Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book ? A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data ? Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. ? Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn ? Learn how to tap into data from diverse social media platforms using the R ecosystem ? Use social media data to formulate and solve real-world problems ? Analyze user social networks and communities using concepts from graph theory and network analysis ? Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels ? Understand the art of representing actionable insights with effective visualizations ? Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on ? Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

Do amazing things with the shell About This Book ? Become an expert in creating powerful shell *s and explore the full possibilities of the shell ? Automate any administrative task you could imagine, with shell *s ? Packed with easy-to-follow recipes on new features on Linux, particularly, Debian-based, to help you accomplish even the most complex tasks with ease Who This Book Is For If you are a beginner or an intermediate Linux user who wants to master the skill of quickly writing *s and automate tasks without reading the entire man pages, then this book is for you. You can start writing *s and one-liners by simply looking at the relevant recipe and its de*ions without any working knowledge of shell *ing or Linux. Intermediate / advanced users, system administrators / developers, and programmers can use this book as a reference when they face problems while coding. What You Will Learn ? Interact with websites via *s ? Write shell *s to mine and process data from the Web ? Automate system backups and other repetitive tasks with crontab ? Create, compress, and encrypt archives of your critical data. ? Configure and monitor Ethernet and wireless networks ? Monitor and log network and system activity ? Tune your system for optimal performance ? Improve your system's security ? Identify resource hogs and network bottlenecks ? Extract audio from video files ? Create web photo albums ? Use git or fossil to manage revision control and interact with FOSS projects ? Create and maintain Linux containers and Virtual Machines ? Run a private Cloud server In Detail The shell is the most powerful tool your computer provides. Despite having it at their fingertips, many users are unaware of how much the shell can accomplish. Using the shell, you can generate databases and web pages from sets of files, automate monotonous admin tasks such as system backups, monitor your system's health and activity, identify network bottlenecks and system resource hogs, and more. This book will show you how to do all this and much more. This book, now in its third edition, describes the exciting new features in the newest Linux distributions to help you accomplish more than you imagine. It shows how to use simple commands to automate complex tasks, automate web interactions, download videos, set up containers and cloud servers, and even get free SSL certificates. Starting with the basics of the shell, you will learn simple commands and how to apply them to real-world issues. From there, you'll learn text processing, web interactions, network and system monitoring, and system tuning. Software engineers will learn how to examine system applications, how to use modern software management tools such as git and fossil for their own work, and how to submit patches to open-source projects. Finally, you'll learn how to set up Linux Containers and Virtual machines and even run your own Cloud server with a free SSL Certificate from letsencrypt.org. Style and approach This book will take you through useful real-world recipes designed to make your daily life easier when working with the shell.

Learn how to develop web applications that deploy cross-platform and are optimized for high performance using ASP.NET Core 2 About This Book ? Master high-level web app performance improvement techniques using ASP.NET Core 2.0 ? Find the right balance between premature optimization and inefficient code ? Design workflows that run asynchronously and are resilient to transient performance issues Who This Book Is For This book is aimed for readers who can build a web application and have some experience with ASP.NET or some other web application framework (such as Ruby on Rails or Django). They can be people who are happy learning details independently but who struggle to discover the topics that they should be researching. The reader should be interested in improving the performance of their web app and in learning about ASP.NET Core and modern C#. What You Will Learn ? Understand ASP.NET Core 2 and how it differs from its predecessor ? Address performance issues at the early stages of development ? Set up development environments on Windows, Mac, and Linux ? Measure, profile and find the most significant problems ? Identify the differences between development workstations and production infrastructures, and how these can exacerbate problems ? Boost the performance of your application but with an eye to how it affects complexity and maintenance ? Explore a few cutting-edge techniques such as advanced hashing and custom transports In Detail The ASP.NET Core 2 framework is used to develop high-performance and cross-platform web applications. It is built on .NET Core 2 and includes significantly more framework APIs than version 1. This book addresses high-level performance improvement techniques. It starts by showing you how to locate and measure problems and then shows you how to solve some of the most common ones. Next, it shows you how to get started with ASP.NET Core 2 on Windows, Mac, Linux, and with Docker containers. The book illustrates what problems can occur as latency increases when deploying to a cloud infrastructure. It also shows you how to optimize C# code and choose the best data structures for the job. It covers new features in C# 6 and 7, along with parallel programming and distributed architectures. By the end of this book, you will be fixing latency issues and optimizing performance problems, but you will also know how this affects the complexity and maintenance of your application. Finally, we will explore a few highly advanced techniques for further optimization. Style and approach A step-by-step practical guide filled with real-world use cases and examples

Learn to implement DevOps using Docker & Kubernetes. About This Book ? Learning DevOps, container, and Kubernetes within one book. ? Leverage Kubernetes as a platform to deploy, scale, and run containers efficiently. ? A practical guide towards container management and orchestration Who This Book Is For This book is targeted for anyone, who wants to learn containerization and clustering in a practical way using Kubernetes. No prerequisite skills required, however, essential DevOps skill and public/private Cloud knowledge will accelerate the reading speed. If you’re advanced readers, you can also get a deeper understanding of all the tools and technique described in the book. What You Will Learn ? Learn fundamental and advanced DevOps skills and tools ? Get a comprehensive understanding for container ? Learn how to move your application to container world ? Learn how to manipulate your application by Kubernetes ? Learn how to work with Kubernetes in popular public cloud ? Improve time to market with Kubernetes and Continuous Delivery ? Learn how to monitor, log, and troubleshoot your application with Kubernetes In Detail Containerization is said to be the best way to implement DevOps. Google developed Kubernetes, which orchestrates containers efficiently and is considered the frontrunner in container orchestration. Kubernetes is an orchestrator that creates and manages your containers on clusters of servers. This book will guide you from simply deploying a container to administrate a Kubernetes cluster, and then you will learn how to do monitoring, logging, and continuous deployment in DevOps. The initial stages of the book will introduce the fundamental DevOps and the concept of containers. It will move on to how to containerize applications and deploy them into. The book will then introduce networks in Kubernetes. We then move on to advanced DevOps skills such as monitoring, logging, and continuous deployment in Kubernetes. It will proceed to introduce permission control for Kubernetes resources via attribute-based access control and role-based access control. The final stage of the book will cover deploying and managing your container clusters on the popular public cloud Amazon Web Services and Google Cloud Platform. At the end of the book, other orchestration frameworks, such as Docker Swarm mode, Amazon ECS, and Apache Mesos will be discussed. Style and approach Readers will be taken through fundamental DevOps skills and Kubernetes concept and administration with detailed examples. It introduces comprehensive DevOps topics, including microservices, automation tools, containers, monitoring, logging, continuous delivery, and popular public cloud environments. At each step readers will learn how to leverage Kubernetes in their everyday lives and transform their original delivery pipeline for fast and efficient delivery.