Build next-generation Artificial Intelligence systems with Java About This Book ? Implement AI techniques to build smart applications using Deeplearning4j ? Perform big data analytics to derive quality insights using Spark MLlib ? Create self-learning systems using neural networks, NLP, and reinforcement learning Who This Book Is For This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus. What You Will Learn ? Manage Artificial Intelligence techniques for big data with Java ? Build smart systems to analyze data for enhanced customer experience ? Learn to use Artificial Intelligence frameworks for big data ? Understand complex problems with algorithms and Neuro-Fuzzy systems ? Design stratagems to leverage data using Machine Learning process ? Apply Deep Learning techniques to prepare data for modeling ? Construct models that learn from data using open source tools ? Analyze big data problems using scalable Machine Learning algorithms In Detail In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java

Explore the features included in the latest versions of MQTT for IoT and M2M communications and use them with modern Python 3. About This Book ? Make your connected devices less prone to attackers by understanding security mechanisms ? Take advantage of MQTT features for IoT and Machine-to-Machine communications ? The only book that covers MQTT with a single language, Python Who This Book Is For This book is for developers who want to learn about the MQTT protocol for their IoT projects. Prior knowledge of working with IoT and Python will be helpful. What You Will Learn ? Learn how MQTT and its lightweight messaging system work ? Understand the MQTT puzzle: clients, servers (formerly known as brokers), and connections ? Explore the features included in the latest versions of MQTT for IoT and M2M communications ? Publish and receive MQTT messages with Python ? Learn the difference between blocking and threaded network loops ? Take advantage of the last will and testament feature ? Work with cloud-based MQTT interfaces in Python In Detail MQTT is a lightweight messaging protocol for small sensors and mobile devices. This book explores the features of the latest versions of MQTT for IoT and M2M communications, how to use them with Python 3, and allow you to interact with sensors and actuators using Python. The book begins with the specific vocabulary of MQTT and its working modes, followed by installing a Mosquitto MQTT broker. You will use different utilities and diagrams to understand the most important concepts related to MQTT. You will learn to?make all the necessary configuration to work with digital certificates for encrypting all data sent between the MQTT clients and the server. You will also work with the different Quality of Service levels and later analyze and compare their overheads. You will write Python 3.x code to control a vehicle with MQTT messages delivered through encrypted connections (TLS 1.2), and learn how leverage your knowledge of the MQTT protocol to build a solution based on requirements. Towards the end, you will write Python code to use the PubNub cloud-based real-time MQTT provider to monitor a surfing competition. In the end, you will have a solution that was built from scratch by analyzing the requirements and then write Python code that will run on water-proof IoT boards connected to multiple sensors in surfboards. Style and approach This book shows you what MQTT is, and how to install and secure an MQTT server. You will write Python 3 code to control a vehicle with MQTT messages, test and improve, then monitor a surfing competition with cloud-based real-time MQTT providers.

Use Java features such as JAX-RS, EJBs, and JPAs to build powerful middleware for newer architectures such as the cloud About This Book ? Explore EJBs to build middleware solutions for enterprise and distributed applications ? Understand middleware designs such as event-based and message-driven web services ? Learn to design and maintain large-scale systems and vendor disputes Who This Book Is For Enterprise architects, designers, developers, and programmers who are interested in learning how to build robust middleware solutions for enterprise software will find this book useful. Prior knowledge of Java EE is essential What You Will Learn ? Implement the latest Java EE 8 APIs and manage dependencies with CDI 2.0 ? Perform CRUD operations and access databases with JPA 2.1 ? Use bean validation API 2.0 to validate data ? Develop business logic with EJB 3.2 ? Incorporate the REST architecture and RESTful API design patterns ? Perform serialization and deserialization on JSON documents using JSON-B ? Utilize JMS for messaging and queuing models and securing applications ? Test applications using JUnit and Mockito and deploy them using Docker In Detail Middleware is the infrastructure in software based applications that enables businesses to solve problems, operate more efficiently, and make money. As the use of middleware extends beyond a single application, the importance of having it written by experts increases substantially. This book will help you become an expert in developing middleware for a variety of applications. The book starts off by exploring the latest Java EE 8 APIs with newer features and managing dependencies with CDI 2.0. You will learn to implement object-to-relational mapping using JPA 2.1 and validate data using bean validation. You will also work with different types of EJB to develop business logic, and with design RESTful APIs by utilizing different HTTP methods and activating JAX-RS features in enterprise applications. You will learn to secure your middleware with Java Security 1.0 and implement various authentication techniques, such as OAuth authentication. In the concluding chapters, you will use various test technologies, such as JUnit and Mockito, to test applications, and Docker to deploy your enterprise applications. By the end of the book, you will be proficient in developing robust, effective, and distributed middleware for your business. Style and approach Learn how to design and implement professional enterprise middleware solutions using the latest techniques and features provided by the Java EE 8 platform.

This book will give you an all encompassing view of the domain name ecosystem combined with a comprehensive set of operations strategies. About This Book ? Manage infrastructure, risk, and management of DNS name servers. Get hands-on with factors like types of name servers, DNS queries and and so on. ? Practical guide for system administrators to manage mission-critical servers ? Based on real-world experience - Written by an industry veteran who has made every possible mistake within this field. Who This Book Is For Ideal for sysadmins, webmasters, IT consultants, and developers-anyone responsible for maintaining your organization's core DNS What You Will Learn ? Anatomy of a domain - how a domain is the sum of both its DNS zone and its registration data, and why that matters. ? The domain name ecosystem - the role of registries, registrars and oversight bodies and their effect on your names. ? How DNS queries work - queries and responses are examined including debugging techniques to zero in on problems. ? Nameserver considerations - alternative nameserver daemons, numbering considerations, and deployment architectures. ? DNS use cases - the right way for basic operations such as domain transfers, large scale migrations, GeoDNS, Anycast DNS. ? Securing your domains - All aspects of security from registrar vendor selection, to DNSSEC and DDOS mitigation strategies. In Detail Managing your organization's naming architecture and mitigating risks within complex naming environments is very important. This book will go beyond looking at “how to run a name server” or “how to DNSSEC sign a domain”, Managing Mission Critical Domains & DNS looks across the entire spectrum of naming; from external factors that exert influence on your domains to all the internal factors to consider when operating your DNS. The readers are taken on a comprehensive guided tour through the world of naming: from understanding the role of registrars and how they interact with registries, to what exactly is it that ICANN does anyway? Once the prerequisite knowledge of the domain name ecosystem is acquired, the readers are taken through all aspects of DNS operations. Whether your organization operates its own nameservers or utilizes an outsourced vendor, or both, we examine the complex web of interlocking factors that must be taken into account but are too frequently overlooked. By the end of this book, our readers will have an end to end to understanding of all the aspects covered in DNS name servers. Style and approach The book is divided into two parts, the first part looks at the wider domain name ecosystem: registries, registrars and oversight policies. The second and larger part goes into operations. Every aspect of naming is considered from the viewpoint of how this affects ones domains, what are the ramifications of different operating methods as portfolios scale.

Explore the latest features of Unity 2018 to create immersive VR projects for Oculus Rift, HTC Vive, Daydream and Gear VR About This Book ? A project-based guide to teach you how to develop immersive and fun VR applications using Unity 3D ? Build experiences with interactable objects, physics, UI, animations, C# scripting, and other Unity features ? Explore the world of VR by building experiences such as diorama, first-person characters, 360-degree projections, social VR, audio fireball game, and VR storytelling Who This Book Is For If you're a non-programmer unfamiliar with 3D computer graphics, or experienced in both but new to virtual reality, and are interested in building your own VR games or applications, then this book is for you. Any experience in Unity is an advantage. What You Will Learn ? Create 3D scenes with Unity and other 3D tools while learning about world space and scale ? Build and run VR applications for specific headsets, including Oculus, Vive, and Daydream ? Interact with virtual objects using eye gaze, hand controllers, and user input events ? Move around your VR scenes using locomotion and teleportation ? Implement an audio fireball game using physics and particle systems ? Implement an art gallery tour with teleportation and data info ? Design and build a VR storytelling animation with a soundtrack and timelines ? Create social VR experiences with Unity networking In Detail Unity has become the leading platform for building virtual reality games, applications, and experiences for this new generation of consumer VR devices. Unity Virtual Reality Projects walks you through a series of hands-on tutorials and in-depth discussions on using the Unity game engine. With its practical and project-based approach, this book will get you up to speed with the specifics of Virtual Reality development in Unity. You will learn how to use Unity to develop VR applications that can be experienced with devices such as Oculus, Daydream, and Vive. Among the many topics and projects, you will explore gaze-based versus hand controller input, world space UI canvases, locomotion and teleportation, software design patterns, 360-degree media, timeline animation, and multiplayer networking. You will learn the Unity 3D game engine via the interactive Unity Editor as well as C# programming. By the end of the book, you will be fully equipped to develop rich, interactive virtual reality experiences using Unity. Style and approach A practical step-by-step guide to building impressive VR experiences with Unity 2018.

Build applications and infrastructures that leverage Function-as-a-Service and Docker About This Book ? Implement containerization in Serverless/FaaS environments ? Utilize Docker as a functional unit of work for Serverless/FaaS platforms ? Use Docker as a portable infrastructure for Serverless Applications Who This Book Is For If you are a Developer, a Docker Engineer, a DevOps Engineer, or any stakeholder interested in learning the use of Docker on Serverless environments then this book is for you. What You Will Learn ? Learn what Serverless and FaaS applications are ? Get acquainted with the architectures of three major serverless systems ? Explore how Docker technologies can help develop Serverless applications ? Create and maintain FaaS infrastructures ? Set up Docker infrastructures to serve as on-premises FaaS infrastructures ? Define functions for Serverless applications with Docker containers In Detail Serverless applications have gained a lot of popularity among developers and are currently the buzzwords in the tech market. Docker and serverless are two terms that go hand-in-hand. This book will start by explaining serverless and Function-as-a-Service (FaaS) concepts, and why they are important. Then, it will introduce the concepts of containerization and how Docker fits into the Serverless ideology. It will explore the architectures and components of three major Docker-based FaaS platforms, how to deploy and how to use their CLI. Then, this book will discuss how to set up and operate a production-grade Docker cluster. We will cover all concepts of FaaS frameworks with practical use cases, followed by deploying and orchestrating these serverless systems using Docker. Finally, we will also explore advanced topics and prototypes for FaaS architectures in the last chapter. By the end of this book, you will be in a position to build and deploy your own FaaS platform using Docker. Style and approach A practical guide that offers a simple way to easily understand Serverless Applications utilizing Docker as the development environment.

Learn the most powerful and primary programming language for writing smart contracts and find out how to write, deploy, and test smart contracts in Ethereum. About This Book ? Get you up and running with Solidity Programming language ? Build Ethereum Smart Contracts with Solidity as your scripting language ? Learn to test and deploy the smart contract to your private Blockchain Who This Book Is For This book is for anyone who would like to get started with Solidity Programming for developing an Ethereum smart contract. No prior knowledge of EVM is required. What You Will Learn ? Learn the basics and foundational concepts of Solidity and Ethereum ? Explore the Solidity language and its uniqueness in depth ? Create new accounts and submit transactions to blockchain ? Get to know the complete language in detail to write smart contracts ? Learn about major tools to develop and deploy smart contracts ? Write defensive code using exception handling and error checking ? Understand Truffle basics and the debugging process In Detail Solidity is a contract-oriented language whose syntax is highly influenced by JavaScript, and is designed to compile code for the Ethereum Virtual Machine. Solidity Programming Essentials will be your guide to understanding Solidity programming to build smart contracts for Ethereum and blockchain from ground-up. We begin with a brief run-through of blockchain, Ethereum, and their most important concepts or components. You will learn how to install all the necessary tools to write, test, and debug Solidity contracts on Ethereum. Then, you will explore the layout of a Solidity source file and work with the different data types. The next set of recipes will help you work with operators, control structures, and data structures while building your smart contracts. We take you through function calls, return types, function modifers, and recipes in object-oriented programming with Solidity. Learn all you can on event logging and exception handling, as well as testing and debugging smart contracts. By the end of this book, you will be able to write, deploy, and test smart contracts in Ethereum. This book will bring forth the essence of writing contracts using Solidity and also help you develop Solidity skills in no time. Style and approach Solidity is a high-level programming language best understood using examples. After covering basic concepts of Ethereum and Solidity, programming constructs will be explained with help of examples. As chapters progress, deployment, usage and testing of contacts will form major aspect of the book. Troubleshooting and unit testing is an important exercise and skill to master this language will also be covered in this book.

Building efficient Python applications at minimal cost by adopting serverless architectures About This Book ? Design and set up a data flow between cloud services and custom business logic ? Make your applications efficient and reliable using serverless architecture ? Build and deploy scalable serverless Python APIs Who This Book Is For This book is for Python developers who would like to learn about serverless architecture. Python programming knowledge is assumed. What You Will Learn ? Understand how AWS Lambda and Microsoft Azure Functions work and use them to create an application ? Explore various triggers and how to select them, based on the problem statement ? Build deployment packages for Lambda functions ? Master the finer details about building Lambda functions and versioning ? Log and monitor serverless applications ? Learn about security in AWS and Lambda functions ? Scale up serverless applications to handle huge workloads and serverless distributed systems in production ? Understand SAM model deployment in AWS Lambda In Detail Serverless architectures allow you to build and run applications and services without having to manage the infrastructure. Many companies have adopted this architecture to save cost and improve scalability. This book will help you design serverless architectures for your applications with AWS and Python. The book is divided into three modules. The first module explains the fundamentals of serverless architecture and how AWS lambda functions work. In the next module, you will learn to build, release, and deploy your application to production. You will also learn to log and test your application. In the third module, we will take you through advanced topics such as building a serverless API for your application. You will also learn to troubleshoot and monitor your app and master AWS lambda programming concepts with API references. Moving on, you will also learn how to scale up serverless applications and handle distributed serverless systems in production. By the end of the book, you will be equipped with the knowledge required to build scalable and cost-efficient Python applications with a serverless framework. Style and approach The book takes a pragmatic approach, using a real-world example to demonstrate building efficient, secure, and scalable serverless applications.

Leverage the power of Python and its de facto GUI framework to build highly interactive interfaces About This Book ? The fundamentals of Python and GUI programming with Tkinter. ? Create multiple cross-platform projects by integrating a host of third-party libraries and tools. ? Build beautiful and highly-interactive user interfaces that target multiple devices. Who This Book Is For This book is for beginners to GUI programming who haven’t used Tkinter yet and are eager to start building great-looking and user-friendly GUIs. Prior knowledge of Python programming is expected. What You Will Learn ? Create a scrollable frame via theCanvas widget ? Use the pack geometry manager andFrame widget to control layout ? Learn to choose a data structurefor a game ? Group Tkinter widgets, such asbuttons, canvases, and labels ? Create a highly customizablePython editor ? Design and lay out a chat window In Detail Tkinter is a modular, cross-platform application development toolkit for Python. When developing GUI-rich applications, the most important choices are which programming language(s) and which GUI framework to use. Python and Tkinter prove to be a great combination. This book will get you familiar with Tkinter by having you create fun and interactive projects. These projects have varying degrees of complexity. We'll start with a simple project, where you'll learn the fundamentals of GUI programming and the basics of working with a Tkinter application. After getting the basics right, we'll move on to creating a project of slightly increased complexity, such as a highly customizable Python editor. In the next project, we'll crank up the complexity level to create an instant messaging app. Toward the end, we'll discuss various ways of packaging our applications so that they can be shared and installed on other machines without the user having to learn how to install and run Python programs. Style and approach Step by Step guide with real world examples

A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript About This Book ? Solve complex computational problems in browser with JavaScript ? Teach your browser how to learn from rules using the power of machine learning ? Understand discoveries on web interface and API in machine learning Who This Book Is For This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book. What You Will Learn ? Get an overview of state-of-the-art machine learning ? Understand the pre-processing of data handling, cleaning, and preparation ? Learn Mining and Pattern Extraction with JavaScript ? Build your own model for classification, clustering, and prediction ? Identify the most appropriate model for each type of problem ? Apply machine learning techniques to real-world applications ? Learn how JavaScript can be a powerful language for machine learning In Detail In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. Style and approach This is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with JavaScript using clear and practical examples.

Be an adaptive thinker that leads the way to Artificial Intelligence About This Book ? AI-based examples to guide you in designing and implementing machine intelligence ? Develop your own method for future AI solutions ? Acquire advanced AI, machine learning, and deep learning design skills Who This Book Is For Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book. What You Will Learn ? Use adaptive thinking to solve real-life AI case studies ? Rise beyond being a modern-day factory code worker ? Acquire advanced AI, machine learning, and deep learning designing skills ? Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology ? Understand future AI solutions and adapt quickly to them ? Develop out-of-the-box thinking to face any challenge the market presents In Detail Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Artificial Intelligence

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda About This Book ? Use Anaconda to find solutions for clustering, classification, and linear regression ? Analyze your data efficiently with the most powerful data science stack ? Use the Anaconda cloud to store, share, and discover projects and libraries Who This Book Is For Hands-On Data Science with Anaconda is for you if you are a developer who is looking for the best tools in the market to perform data science. It’s also ideal for data analysts and data science professionals who want to improve the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected. What You Will Learn ? Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda ? Use the package manager conda and discover, install, and use functionally efficient and scalable packages ? Get comfortable with heterogeneous data exploration using multiple languages within a project ? Perform distributed computing and use Anaconda Accelerate to optimize computational powers ? Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud ? Tackle advanced data prediction problems In Detail Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R. Style and approach This book is your step-by-step guide full of use cases, examples and illustrations to get you well-versed with the concepts of Anaconda.

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow v1.7. About This Book ? Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow v1.7 ? Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide ? Gain real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is for 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 ? Apply deep machine intelligence and GPU computing with TensorFlow v1.7 ? Access public datasets and use TensorFlow to load, process, and transform the data ? Discover how to use the high-level TensorFlow API to build more powerful applications ? Use 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 In Detail Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow v1.7, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. Style and approach This step-by-step guide explores common, and not so common, deep neural networks, and shows how they can be exploited in the real world with complex raw data. Benefit from practical examples, and learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.

Echo is a leading framework for creating web applications with the Go language. This book will show you how to develop scalable real-world web apps, RESTful services, and backend systems with Echo. About This Book ? The easiest way to learn how to build web apps with Echo ? Build a full working project ? For Go developers with only basic web development knowledge required Who This Book Is For You will need to know the basics of the Go language, and the general concepts of web development. What You Will Learn ? Key design considerations for high performance Echo applications ? How Echo handles routing ? How context is managed through the lifetime of the request and response pipeline ? Decrease complexity of your apps by developing middleware functions ? Interact with the request through request data bindings ? Interact with the response through response data renderings within the framework ? Use Echo's logging and error handling facilities ? Render Go templates within Echo to allow for server side rendering of content In Detail Echo is a leading framework for creating web applications with the Go language.? This book will show you how to develop scalable real-world web apps, RESTful services, and backend systems with Echo.? After a thorough understanding of the basics, you'll be introduced to all the concepts for a building real-world web system with Echo. You will start with the the Go HTTP standard library, and setting up your work environment. You will move on to Echo handlers, group routing, data binding, and middleware processing. After that, you will learn how to test your Go application and use templates.? By the end of this book you will be able to build your very own high performance apps using Echo. A Quick Start Guide is a focussed, shorter title which provides a faster paced introduction to a technology. They are for people who don’t need all the detail at this point in their learning curve. The presentation has been streamlined to concentrate on the things you really need to know, rather than everything. Style and approach This book creates a working example of a web application written with the Echo framework, and shows you enough of Echo to give context for a developer to bootstrap a high performance web application with the smallest amount of development time.

Learn how to automate and manage your containers and reduce the overall operation burden on your system. About This Book ? Use containers to manage, scale and orchestrate apps in your organization ? Transform the latest concept of Kubernetes 1.10 into examples ? Expert techniques for orchestrating containers effectively Who This Book Is For This book is for system administrators, developers, DevOps engineers, or any stakeholder who wants to understand how Kubernetes works using a recipe-based approach. Basic knowledge of Kubernetes and Containers is required. What You Will Learn ? Build your own container cluster ? Deploy and manage highly scalable, containerized applications with Kubernetes ? Build high-availability Kubernetes clusters ? Build a continuous delivery pipeline for your application ? Track metrics and logs for every container running in your cluster ? Streamline the way you deploy and manage your applications with large-scale container orchestration In Detail Kubernetes is an open source orchestration platform to manage containers in a cluster environment. With Kubernetes, you can configure and deploy containerized applications easily. This book gives you a quick brush up on how Kubernetes works with containers, and an overview of main Kubernetes concepts, such as Pods, Deployments, Services and etc. This book explains how to create Kubernetes clusters and run applications with proper authentication and authorization configurations. With real-world recipes, you'll learn how to create high availability Kubernetes clusters on AWS, GCP and in on-premise datacenters with proper logging and monitoring setup. You'll also learn some useful tips about how to build a continuous delivery pipeline for your application. Upon completion of this book, you will be able to use Kubernetes in production and will have a better understanding of how to manage containers using Kubernetes. Style and approach This recipe-based book will teach you how to use Kubernetes in production and will help you discover various steps involved in managing your containers using Kubernetes

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 scalable, reliable, and cost-effective applications with a serverless architecture About This Book ? Design a real-world serverless application from scratch ? Learn about AWS Lambda function and how to use Lambda functions to glue other AWS Services ? Use the Java programming language and well-known design patterns. Although Java is used for the examples in this book, the concept is applicable across all languages ? Learn to migrate your JAX-RS application to AWS Lambda and API Gateway Who This Book Is For This book is for developers and software architects who are interested in designing on the back end. Since the book uses Java to teach concepts, knowledge of Java is required. What You Will Learn ? Learn to form microservices from bigger Softwares ? Orchestrate and scale microservices ? Design and set up the data flow between cloud services and custom business logic ? Get to grips with cloud provider’s APIs, limitations, and known issues ? Migrate existing Java applications to a serverless architecture ? Acquire deployment strategies ? Build a highly available and scalable data persistence layer ? Unravel cost optimization techniques In Detail Over the past years, all kind of companies from start-ups to giant enterprises started their move to public cloud providers in order to save their costs and reduce the operation effort needed to keep their shops open. Now it is even possible to craft a complex software system consisting of many independent micro-functions that will run only when they are needed without needing to maintain individual servers. The focus of this book is to design serverless architectures, and weigh the advantages and disadvantages of this approach, along with decision factors to consider. You will learn how to design a serverless application, get to know that key points of services that serverless applications are based on, and known issues and solutions. The book addresses key challenges such as how to slice out the core functionality of the software to be distributed in different cloud services and cloud functions. It covers basic and advanced usage of these services, testing and securing the serverless software, automating deployment, and more. By the end of the book, you will be equipped with knowledge of new tools and techniques to keep up with this evolution in the IT industry. Style and approach The book takes a pragmatic approach, showing you all the examples you need to build efficient serverless applications.

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.

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.