Exploit the visualization capabilities of Kibana and build powerful interactive dashboards About This Book Introduction to data-driven architecture and the Elastic stack Build effective dashboards for data visualization and explore datasets with Elastic Graph A comprehensive guide to learning scalable data visualization techniques in Kibana Who This Book Is For If you are a developer, data visualization engineer, or data scientist who wants to get the best of data visualization at scale then this book is perfect for you. A basic understanding of Elasticsearch and Logstash is required to make the best use of this book. What You Will Learn How to create visualizations in Kibana Ingest log data, structure an Elasticsearch cluster, and create visualization assets in Kibana Embed Kibana visualization on web pages Scaffold, develop, and deploy new Kibana & Timelion customizations Build a metrics dashboard in Timelion based on time series data Use the Graph plugin visualization feature and leverage a graph query Create, implement, package, and deploy a new custom plugin Use Prelert to solve anomaly detection challenges In Detail Kibana is an open source data visualization platform that allows you to interact with your data through stunning, powerful graphics. Its simple, browser-based interface enables you to quickly create and share dynamic dashboards that display changes to Elasticsearch queries in real time. In this book, you’ll learn how to use the Elastic stack on top of a data architecture to visualize data in real time. All data architectures have different requirements and expectations when it comes to visualizing the data, whether it’s logging analytics, metrics, business analytics, graph analytics, or scaling them as per your business requirements. This book will help you master Elastic visualization tools and adapt them to the requirements of your project. You will start by learning how to use the basic visualization features of Kibana 5. Then you will be shown how to implement a pure metric analytics architecture and visualize it using Timelion, a very recent and trendy feature of the Elastic stack. You will learn how to correlate data using the brand-new Graph visualization and build relationships between documents. Finally, you will be familiarized with the setup of a Kibana development environment so that you can build a custom Kibana plugin. By the end of this book you will have all the information needed to take your Elastic stack skills to a new level of data visualization. Style and approach This book takes a comprehensive, step-by-step approach to working with the visualization aspects of the Elastic stack. Every concept is presented in a very easy-to-follow manner that shows you both the logic and method of implementation. Real world cases are referenced to highlight how each of the key concepts can be put to practical use.

Build, implement and scale distributed deep learning models for large-scale datasets About This Book Get to grips with the deep learning concepts and set up Hadoop to put them to use Implement and parallelize deep learning models on Hadoop’s YARN framework A comprehensive tutorial to distributed deep learning with Hadoop Who This Book Is For If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book. What You Will Learn Explore Deep Learning and various models associated with it Understand the challenges of implementing distributed deep learning with Hadoop and how to overcome it Implement Convolutional Neural Network (CNN) with deeplearning4j Delve into the implementation of Restricted Boltzmann Machines (RBM) Understand the mathematical explanation for implementing Recurrent Neural Networks (RNN) Get hands on practice of deep learning and their implementation with Hadoop. In Detail This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning. In this book, you will also learn how to overcome the challenges that you face while implementing distributed deep learning with large-scale unstructured datasets. The book will also show you how you can implement and parallelize the widely used deep learning models such as Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Restricted Boltzmann Machines and autoencoder using the popular deep learning library deeplearning4j. Get in-depth mathematical explanations and visual representations to help you understand the design and implementations of Recurrent Neural network and Denoising AutoEncoders with deeplearning4j. To give you a more practical perspective, the book will also teach you the implementation of large-scale video processing, image processing and natural language processing on Hadoop. By the end of this book, you will know how to deploy various deep neural networks in distributed systems using Hadoop. Style and approach This book takes a comprehensive, step-by-step approach to implement efficient deep learning models on Hadoop. It starts from the basics and builds the readers’ knowledge as they strengthen their understanding of the concepts. Practical examples are included in every step of the way to supplement the theory.

Walmart's OneOps is an open source DevOps platform that is used for cloud and application lifecycle management. It can manage critical and complex application workload on any multi cloud-based infrastructure and revolutionizes the way administrators, developers, and engineers develop and launch new products. This practical book focuses on real-life cases and hands-on scenarios to develop, launch, and test your applications faster, so you can implement the DevOps process using OneOps. You will be exposed to the fundamental aspects of OneOps starting with installing, deploying, and configuring OneOps in a test environment, which will also come in handy later for development and debugging. You will also learn about design and architecture, and work through steps to perform enterprise level deployment. You will understand the initial setup of OneOps such as creating organization, teams, and access management. Finally, you will be taught how to configure, repair, scale, and extend applications across various cloud platforms. What you will learn ?See how to install OneOps ?Configure OneOps including creating and configuring organization and teams ?Work through practical deployment scenarios ?Maintain OneOps environments including backups and logs ?Build custom components for OneOps ?Extend OneOps by calling the REST API

Gain a deep understanding of the complexity of data structures and algorithms and discover the right way to write more efficient code About This Book ?This book provides complete coverage of reactive and functional data structures ?Based on the latest version of Java 9, this book illustrates the impact of new features on data structures ?Gain exposure to important concepts such as Big-O Notation and Dynamic Programming Who This Book Is For This book is for Java developers who want to learn about data structures and algorithms. Basic knowledge of Java is assumed. What You Will Learn ?Understand the fundamentals of algorithms, data structures, and measurement of complexity ?Find out what general purpose data structures are, including arrays, linked lists, double ended linked lists, and circular lists ?Get a grasp on the basics of abstract data types―stack, queue, and double ended queue ?See how to use recursive functions and immutability while understanding and in terms of recursion ?Handle reactive programming and its related data structures ?Use binary search, sorting, and efficient sorting―quicksort and merge sort ?Work with the important concept of trees and list all nodes of the tree, traversal of tree, search trees, and balanced search trees ?Apply advanced general purpose data structures, priority queue-based sorting, and random access immutable linked lists ?Gain a better understanding of the concept of graphs, directed and undirected graphs, undirected trees, and much more In Detail Java

Create and maintain powerful Bash scripts for automation and administration. Key Features *Get up and running with Linux shell scripting using real-world examples *Leverage command-line techniques and methodologies to automate common yet complex administration tasks *A practical guide with exposure to scripting constructs and common scripting patterns Book Description Shell scripts allow us to program commands in chains and have the system execute them as a scripted event, just like batch files. This book will start with an overview of Linux and Bash shell scripting, and then quickly deep dive into helping you set up your local environment, before introducing you to tools that are used to write shell scripts. The next set of chapters will focus on helping you understand Linux under the hood and what Bash provides the user. Soon, you will have embarked on your journey along the command line. You will now begin writing actual scripts instead of commands, and will be introduced to practical applications for scripts. The final set of chapters will deep dive into the more advanced topics in shell scripting. These advanced topics will take you from simple scripts to reusable, valuable programs that exist in the real world. The final chapter will leave you with some handy tips and tricks and, as regards the most frequently used commands, a cheat sheet containing the most interesting flags and options will also be provided. After completing this book, you should feel confident about starting your own shell scripting projects, no matter how simple or complex the task previously seemed. We aim to teach you how to script and what to consider, to complement the clear-cut patterns that you can use in your daily scripting challenges. What you will learn *Understand Linux and Bash basics as well as shell scripting fundamentals *Learn to write simple shell scripts that interact with Linux operating system *Build, maintain, and deploy scripts in a Linux environment *Learn best practices for writing shell scripts *Avoid common pitfalls associated with Bash scripting *Gain experience and the right toolset to write your own complex shell scripts Who this book is for This book targets new and existing Linux system administrators, Windows system administrators or developers who are interested in automating administrative tasks. No prior shell scripting experience is needed but in case you do this book will make a pro quickly. Readers should have a basic understanding of the command line.

A collection of in-depth conversations with leading developer advocates that reveal the world of developer relations today Key Features * Top developer advocates reveal the work they’re doing at the center of their tech communities and the impact their advocacy is having on the tech industry as a whole * Discover the best practices of developer advocacy and get the inside story on working at some of the world’s largest tech companies * Features contributions from noted developer advocates, including Scott Hanselman, Sally Eaves, Venkat Subramaniam, Jono Bacon, Ted Neward, and more Book Description What exactly is a developer advocate, and how do they connect developers and companies around the world? Why is the area of developer relations set to explode? Can anybody with a passion for tech become a developer advocate? What are the keys to success on a global scale? How does a developer advocate maintain authenticity when balancing the needs of their company and their tech community? What are the hot topics in areas including Java, JavaScript, "tech for good," artificial intelligence, blockchain, the cloud, and open source? These are just a few of the questions addressed by developer advocate and author Geertjan Wielenga in Developer, Advocate!. 32 of the industry's most prominent developer advocates, from companies including Oracle, Microsoft, Google, and Amazon, open up about what it's like to turn a lifelong passion for knowledge sharing about tech into a rewarding career. These advocates run the gamut from working at large software vendors to small start-ups, along with independent developer advocates who work within organizations or for themselves. In Developer, Advocate!, readers will see how developer advocates are actively changing the world, not only for developers, but for individuals and companies navigating the fast-changing tech landscape. More importantly, Developer, Advocate! serves as a rallying cry to inspire and motivate tech enthusiasts and burgeoning developer advocates to get started and take their first steps within their tech community. What you will learn * Discover how developer advocates are putting developer interests at the heart of the software industry in companies including Microsoft and Google * Gain the confidence to use your voice in the tech community * Immerse yourself in developer advocacy techniques * Understand and overcome the challenges and obstacles facing developer advocates today * Hear predictions from the people at the cutting edge of tech * Explore your career options in developer advocacy Who this book is for Anybody interested in developer advocacy, the impact it is having, and how to build developer advocacy capabilities

Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key Features *Bridge your data analysis with the power of programming, complex algorithms, and AI *Use Python and its extensive libraries to power your way to new levels of data insight *Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series *Explore this modern approach across with key industry case studies and hands-on projects Book Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learn *A new toolset that has been carefully crafted to meet for your data analysis challenges *Full and detailed case studies of the toolset across several of today’s key industry contexts *Become super productive with a new toolset across Python and Jupyter Notebook *Look into the future of data science and which directions to develop your skills next Who this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.

Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks Key Features *Understand the foundations of meta learning algorithms *Explore practical examples to explore various one-shot learning algorithms with its applications in TensorFlow *Master state of the art meta learning algorithms like MAML, reptile, meta SGD Book Description Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models. What you will learn *Understand the basics of meta learning methods, algorithms, and types *Build voice and face recognition models using a siamese network *Learn the prototypical network along with its variants *Build relation networks and matching networks from scratch *Implement MAML and Reptile algorithms from scratch in Python *Work through imitation learning and adversarial meta learning *Explore task agnostic meta learning and deep meta learning Who this book is for Hands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.

Harness the power of Redis to integrate and manage your projects efficiently About This Book Learn how to use Redis's data types efficiently to manage large data sets Scale Redis to multiple servers with Twemproxy, Redis Sentinel, and Redis Cluster A fast-paced guide, full of real-world examples to help you get the best out of the features offered by Redis Who This Book Is For If you are a competent developer with experience of working with data structure servers and want to boost your project's performance by learning about features of Redis, then this book is for you. What You Will Learn Build analytics applications using Bitmaps and Hyperloglogs Enhance scalability with Twemproxy, Redis Sentinel, and Redis Cluster Build a Time Series implementation in Node.js and Redis Create your own Redis commands by extending Redis with Lua Get to know security techniques to protect your data (SSL encryption, firewall rules, basic authorization) Persist data to disk and learn the trade-offs of AOF and RDB Understand how to use Node.js, PHP, Python, and Ruby clients for Redis Avoid common pitfalls when designing your next solution In Detail Redis is the most popular in-memory key-value data store. It’s very lightweight and its data types give it an edge over the other competitors. If you need an in-memory database or a high-performance cache system that is simple to use and highly scalable, Redis is what you need. Redis Essentials is a fast-paced guide that teaches the fundamentals on data types, explains how to manage data through commands, and shares experiences from big players in the industry. We start off by explaining the basics of Redis followed by the various data types such as Strings, hashes, lists, and more. Next, Common pitfalls for various scenarios are described, followed by solutions to ensure you do not fall into common traps. After this, major differences between client implementations in PHP, Python, and Ruby are presented. Next, you will learn how to extend Redis with Lua, get to know security techniques such as basic authorization, firewall rules, and SSL encryption, and discover how to use Twemproxy, Redis Sentinel, and Redis Cluster to scale infrastructures horizontally. At the end of this book, you will be able to utilize all the essential features of Redis to optimize your project's performance. Style and approach A practical guide that offers the foundation upon which you can begin to understand the capabilities of Redis using a step-by-step approach. This book is full of real-world problems and in-depth knowledge of the concepts and features of Redis, with plenty of examples.

Extend the capabilities of AngularJS and build dynamic web applications by creating customized directives with this selection of more than 30 recipes About This Book Learn how to extend HTML templates in new ways to build even better web applications with exceptional interface components Build reusable directives for large-scale AngularJS applications Create even sophisticated and impressive modern web apps with ease Who This Book Is For This book is for developers with AngularJS experience who want to extend their knowledge to create or customize directives in any type of AngularJS application. Some experience of modern tools such as Yeoman and Bower would be helpful, but is not a requirement. What You Will Learn Build and customize external HTML templates, and create simple, effective directives for common interface components Learn how to use Controller function and any Bootstrap UI directives to manipulate the DOM and how to transform any UI library into AngularJS directives Construct an AngularJS application to use shared components and validate your HTML5 Discover how to use jQuery events and manipulate the DOM using jQuery UI inside AngularJS applications Create custom directives for ongoing projects using Yeoman generators, and find out how to implement standalone directives Build reusable directives for Large AngularJS applications and extend directives to use dynamic templates Write unit test for directives using the Karma runner and Jasmine’s behavior-driven development framework In Detail AngularJS directives are at the center of what makes it such an exciting – and important - web development framework. With directives, you can take greater control over HTML elements on your web pages – they ‘direct’ Angular’s HTML compiler to behave in the way you want it to. It makes building modern web applications a much more expressive experience, and allows you to focus more closely on improving the way that user interaction impacts the DOM and the way your app manages data. If you’re already using Angular, you probably recognize the power of directives to transform the way you understand and build your projects – but customizing and creating your own directives to harness AngularJS to its full potential can be more challenging. This cookbook shows you how to do just that – it’s a valuable resource that demonstrates how to use directives at every stage in the workflow. Packed with an extensive range of solutions and tips that AngularJS developers shouldn’t do without, you’ll find out how to make the most of directives. You’ll find recipes demonstrating how to build a number of different user interface components with directives, so you can take complete control over how users interact with your application. You’ll also learn how directives can simplify the way you work by creating reusable directives – by customizing them with Yeoman you can be confident that you’re application has the robust architecture that forms the bedrock of the best user experiences. You’ll also find recipes that will help you learn how to unit test directives, so you can be confident in the reliability and performance of your application. Whether you’re looking for guidance to dive deeper into AngularJS directives, or you want a reliable resource, relevant to today’s web development challenges, AngularJS Directives Cookbook delivers everything you need in an easily accessible way. Style and approach This book easy-to-follow guide is packed with hands-on recipes to help you build modular AngularJS applications with custom directives. It presents tips on using the best tools and various ways to use these tools for front-end development.

Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book Build smart extensions to pre-existing features at work that can help maximize their value Quantify your models to drive real improvement Take your knowledge of basic concepts, such as linear regression and Na?ve Bayes classification, to the next level and productionalize their models Play what-if games with your models and techniques by following the test-driven exploration process Who This Book Is For This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. You may be starting, or have already started, a machine learning project at work and are looking for a way to deliver results quickly to enable rapid iteration and improvement. Those looking for examples of how to isolate issues in models and improve them will find ideas in this book to move forward. What You Will Learn Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty Generate complex and simple random data to create a wide variety of test cases that can be codified into tests Develop models iteratively, even when using a third-party library Quantify model quality to enable collaboration and rapid iteration Adopt simpler approaches to common machine learning algorithms Take behaviour-driven development principles to articulate test intent In Detail Machine learning is the process of teaching machines to remember data patterns, using them to predict future outcomes, and offering choices that would appeal to individuals based on their past preferences. Machine learning is applicable to a lot of what you do every day. As a result, you can’t take forever to deliver your first iteration of software. Learning to build machine learning algorithms within a controlled test framework will speed up your time to deliver, quantify quality expectations with your clients, and enable rapid iteration and collaboration. This book will show you how to quantifiably test machine learning algorithms. The very different, foundational approach of this book starts every example algorithm with the simplest thing that could possibly work. With this approach, seasoned veterans will find simpler approaches to beginning a machine learning algorithm. You will learn how to iterate on these algorithms to enable rapid delivery and improve performance expectations. The book begins with an introduction to test driving machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression. You will discover how to test different approaches to na?ve bayes and compare them quantitatively, along with how to apply OOP (Object-Oriented Programming) and OOP patterns to test-driven code, leveraging SciKit-Learn. Finally, you will walk through the development of an algorithm which maximizes the expected value of profit for a marketing campaign by combining one of the classifiers covered with the multiple regression example in the book. Style and approach An example-driven guide that builds a deeper knowledge and understanding of iterative machine learning development, test by test. Each topic develops solutions using failing tests to illustrate problems; these are followed by steps to pass the tests, simply and straightforwardly. Topics which use generated data explore how the data was generated, alongside explanations of the assumptions behind different machine learning techniques.

A simple yet concise step-by-step tutorial that starts from scratch and builds up your knowledge with focused examples that will enable you to set up and run an e-commerce website.This book is for beginners and will take you through the installation and configuration of Drupal Commerce from scratch, but some familiarity with Drupal 7 will be an advantage. All examples are based on development on a local computer – you do not need a hosted Drupal environment.

If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.

If you are a beginner to AndEngine, or mobile game development in general, and you are looking for a simple way to start making games for Android, this book is for you. You should already know the basics of Java programming, but no previous game development experience is required.

A step-by-step guide that spices up your web pages and designs them in the way you want using the most widely used JavaScript library, jQuery. The beginner-friendly and easy-to-understand approach of the book will help get to grips with jQuery in no time. If you know the fundamentals of HTML and CSS, and want to extend your knowledge by learning to use JavaScript, then this is just the book for you. jQuery makes JavaScript straightforward and approachable – you'll be surprised at how easy it can be to add animations and special effects to your beautifully designed pages.

This book provides a step by step approach that explains the most important extension components and their features. All the major features are explained by using the JobHub application with supporting screenshots. If you are an intermediate to advanced level user (or developer) who already has a basic working knowledge of PrimeFaces, then this book is for you. The only thing you need to know is Java Server Faces(JSF).

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

This book is for developers who have knowledge of the basics of the SFML library and its capabilities in 2D game development. Minimal experience with C++ is required.

This book is ideal for system developers with knowledge and experience of embedded systems. Knowledge of BeagleBone Black is assumed, while no knowledge of Yocto Project build system is necessary.

FreeSWITCH 1.6 Cookbook is written for anyone who wants to learn more about using FreeSWITCH in production. The information is presented in such a way that you can get up and running quickly. The cookbook approach eschews much of the foundational concepts, and instead focuses on discrete examples that illustrate specific features. If you need to implement a particular feature as quickly as possible, then this book is for you.

If you are an existing Red Hat administrator who is new to the Red Hat cloud infrastructure and would like to manage and deploy hybrid clouds, then this book is for you. Red Hat Linux administration experience is assumed.