Explore Implementation of core kernel subsystems About This Book ? Master the design, components, and structures of core kernel subsystems ? Explore kernel programming interfaces and related algorithms under the hood ? Completely updated material for the 4.12.10 kernel Who This Book Is For If you are a kernel programmer with a knowledge of kernel APIs and are looking to build a comprehensive understanding, and eager to explore the implementation, of kernel subsystems, this book is for you. It sets out to unravel the underlying details of kernel APIs and data structures, piercing through the complex kernel layers and gives you the edge you need to take your skills to the next level. What You Will Learn ? Comprehend processes and fles—the core abstraction mechanisms of the Linux kernel that promote effective simplification and dynamism ? Decipher process scheduling and understand effective capacity utilization under general and real-time dispositions ? Simplify and learn more about process communication techniques through signals and IPC mechanisms ? Capture the rudiments of memory by grasping the key concepts and principles of physical and virtual memory management ? Take a sharp and precise look at all the key aspects of interrupt management and the clock subsystem ? Understand concurrent execution on SMP platforms through kernel synchronization and locking techniques In Detail Mastering Linux Kernel Development looks at the Linux kernel, its internal arrangement and design, and various core subsystems, helping you to gain significant understanding of this open source marvel. You will look at how the Linux kernel, which possesses a kind of collective intelligence thanks to its scores of contributors, remains so elegant owing to its great design. This book also looks at all the key kernel code, core data structures, functions, and macros, giving you a comprehensive foundation of the implementation details of the kernel’s core services and mechanisms. You will also look at the Linux kernel as well-designed software, which gives us insights into software design in general that are easily scalable yet fundamentally strong and safe. By the end of this book, you will have considerable understanding of and appreciation for the Linux kernel. Style and approach Each chapter begins with the basic conceptual know-how for a subsystem and extends into the details of its implementation. We use appropriate code excerpts of critical routines and data structures for subsystems.

Implement and manage your software-defined, massively scalable storage system About This Book ? Explore Ceph's architecture in order to achieve scalability and high availability ? Learn to utilize Ceph efficiently with the help of practical examples ? Successfully implement Ceph clusters to scale-out storage solutions along with outstanding data protection Who This Book Is For A basic knowledge of GNU/Linux, and storage systems, and server components is assumed. If you have no experience of software-defined storage solutions and Ceph, but are eager to learn about them, this is the book for you. What You Will Learn ? The limitations of existing systems and why you should use Ceph as a storage solution ? Familiarity with Ceph's architecture, components, and services ? Instant deployment and testing of Ceph within a Vagrant and VirtualBox environment ? Ceph operations including maintenance, monitoring, and troubleshooting ? Storage provisioning of Ceph's block, object, and filesystem services ? Integrate Ceph with OpenStack ? Advanced topics including erasure coding, CRUSH maps, and performance tuning ? Best practices for your Ceph clusters In Detail Learning Ceph, Second Edition will give you all the skills you need to plan, deploy, and effectively manage your Ceph cluster. You will begin with the first module, where you will be introduced to Ceph use cases, its architecture, and core projects. In the next module, you will learn to set up a test cluster, using Ceph clusters and hardware selection. After you have learned to use Ceph clusters, the next module will teach you how to monitor cluster health, improve performance, and troubleshoot any issues that arise. In the last module, you will learn to integrate Ceph with other tools such as OpenStack, Glance, Manila, Swift, and Cinder. By the end of the book you will have learned to use Ceph effectively for your data storage requirements. Style and approach This step-by-step guide, including use cases and examples, not only helps you to easily use Ceph but also demonstrates how you can use it to solve any of your server or drive storage issues.

This step-by-step guide will teach you how to use GeoServer to build custom and interactive maps using your data. About This Book ? Exploit the power of GeoServer to provide agile, flexible, and low -cost community projects ? Share real-time maps quickly ? Boost your map server's performance using the power and flexibility of GeoServer Who This Book Is For If you are a web developer with knowledge of server side *ing, have experience in installing applications on the server, and want to go beyond Google Maps by offering dynamically built maps on your site with your latest geospatial data stored in MySQL, PostGIS, MySQL, or Oracle, this is the book for you. What You Will Learn ? Install GeoServer quickly ? Access dynamic real-time geospatial data that you can easily integrate into your own web-based application ? Create custom styles for lines, points, and polygons for great-looking maps ? Command GeoServer remotely using REST ? Tune your GeoServer instance for performance ? Move GeoServer into production ? Learn advanced topics to extend GeoServer's capabilities In Detail GeoServer is an opensource server written in Java that allows users to share, process, and edit geospatial data. This book will guide you through the new features and improvements of GeoServer and will help you get started with it. GeoServer Beginner's Guide gives you the impetus to build custom maps using your data without the need for costly commercial software licenses and restrictions. Even if you do not have prior GIS knowledge, you will be able to make interactive maps after reading this book. You will install GeoServer, access your data from a database, and apply style points, lines, polygons, and labels to impress site visitors with real-time maps. Then you follow a step-by-step guide that installs GeoServer in minutes. You will explore the web-based administrative interface to connect to backend data stores such as PostGIS, and Oracle. Going ahead, you can display your data on web-based interactive maps, use style lines, points, polygons, and embed images to visualize this data for your web visitors. You will walk away from this book with a working application ready for production. After reading GeoServer Beginner's Guide, you will be able to build beautiful custom maps on your website using your geospatial data. Style and approach Step-by-step instructions are included and the needs of a beginner are totally satisfied by the book. The book consists of plenty of examples with accompanying screenshots and code for an easy learning curve.

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book ? Apply R to simplify predictive modeling with short and simple code ? Use machine learning to solve problems ranging from small to big data ? Build a training and testing dataset, applying different classification methods. Who This Book Is For This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. What You Will Learn ? Create and inspect transaction datasets and perform association analysis with the Apriori algorithm ? Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm ? Compare differences between each regression method to discover how they solve problems ? Detect and impute missing values in air quality data ? Predict possible churn users with the classification approach ? Plot the autocorrelation function with time series analysis ? Use the Cox proportional hazards model for survival analysis ? Implement the clustering method to segment customer data ? Compress images with the dimension reduction method ? Incorporate R and Hadoop to solve machine learning problems on big data In Detail Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier. Style and approach This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.

Master realistic animations and graphics, particle systems, game AI and physics, sprites and VR development with Unity 2017 About This Book ? Create professional grade games with realistic animation and graphics, particle systems and game physics with Unity 2017 ? Unleash the power of C# *ing to create intelligent game AI and professional grade game workflows. ? Create immersive VR games using the latest Unity 2017 VR SDK. Who This Book Is For If you are a Unity developer who now wants to develop and deploy interesting games by leveraging the new features of Unity 2017, then this is the book for you. Basic knowledge of C# programming is assumed. What You Will Learn ? Explore hands-on tasks and real-world scenarios to make a Unity horror adventure game ? Create enemy characters that act intelligently and make reasoned decisions ? Use data files to save and restore game data in a way that is platform-agnostic ? Get started with VR development ? Use navigation meshes, occlusion culling, and Profiler tools ? Work confidently with GameObjects, rotations, and transformations ? Understand specific gameplay features such as AI enemies, inventory systems, and level design In Detail Do you want to make the leap from being an everyday Unity developer to being a pro game developer? Then look no further! This book is your one-stop solution to creating mesmerizing games with lifelike features and amazing gameplay. This book focuses in some detail on a practical project with Unity, building a first-person game with many features. You'll delve into the architecture of a Unity game, creating expansive worlds, interesting render effects, and other features to make your games special. You will create individual game components, use efficient animation techniques, and implement collision and physics effectively. Specifically, we'll explore optimal techniques for importing game assets, such as meshes and textures; tips and tricks for effective level design; how to animate and * NPCs; how to configure and deploy to mobile devices; how to prepare for VR development; how to work with version control; and more. By the end of this book, you'll have developed sufficient competency in Unity development to produce fun games with confidence. Style and approach This book takes an easy-to-follow, step-by-step tutorial approach. You will create an advanced level Unity game with an emphasis on leveraging advanced Unity 2017 features while developing the game in its entirety.

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

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*

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

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

"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 "

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

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. About This Book ? Get up and running with the Jupyter ecosystem and some example datasets ? Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests ? Discover how you can use web scraping to gather and parse your own bespoke datasets Who This Book Is For This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start. What You Will Learn ? Identify potential areas of investigation and perform exploratory data analysis ? Plan a machine learning classification strategy and train classification models ? Use validation curves and dimensionality reduction to tune and enhance your models ? Scrape tabular data from web pages and transform it into Pandas DataFrames ? Create interactive, web-friendly visualizations to clearly communicate your findings In Detail Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. Style and approach This book covers every aspect of the standard data-workflow process within a day, along with theory, practical hands-on coding, and relatable illustrations.

A hands-on guide to creating, monitoring, and tuning a high performance Spring web application About This Book ? Understand common performance pitfalls and improve your application's performance ? Build and deploy strategies for complex applications using the microservice architecture ? Understand internals of JVM - the core of all Java Runtime Environments Who This Book Is For If you’re a Spring developer who’d like to build high performance applications and have more control over your application's performance in production and development, this book is for you. Some familiarity with Java, Maven, and Eclipse is necessary. What You Will Learn ? Master programming best practices and performance improvement with bean wiring ? Analyze the performance of various AOP implementations ? Explore database interactions with Spring to optimize design and configuration ? Solve Hibernate performance issues and traps ? Leverage multithreading and concurrent programming to improve application performance ? Gain a solid foundation in JVM performance tuning using various tools ? Learn the key concepts of the microservice architecture and how to monitor them ? Perform Spring Boot performance tuning, monitoring, and health checks In Detail While writing an application, performance is paramount. Performance tuning for real-world applications often involves activities geared toward detecting bottlenecks. The recent release of Spring 5.0 brings major advancements in the rich API provided by the Spring framework, which means developers need to master its tools and techniques to achieve high performance applications. Hands-On High Performance with Spring 5 begins with the Spring framework's core features, exploring the integration of different Spring projects. It proceeds to evaluate various Spring specifications to identify those adversely affecting performance. You will learn about bean wiring configurations, aspect-oriented programming, database interaction, and Hibernate to focus on the metrics that help identify performance bottlenecks. You will also look at application monitoring, performance optimization, JVM internals, and garbage collection optimization. Lastly, the book will show you how to leverage the microservice architecture to build a high performance and resilient application. By the end of the book, you will have gained an insight into various techniques and solutions to build and troubleshoot high performance Spring-based applications. Style and approach This book takes a step-by-step approach with focused examples to teach you how to increase application performance.

Leverage the power of Apple's Core ML to create smart iOS apps About This Book ? Explore the concepts of machine learning and Apple’s Core ML APIs ? Use Core ML to understand and transform images and videos ? Exploit the power of using CNN and RNN in iOS applications Who This Book Is For Machine Learning with Core ML is for you if you are an intermediate iOS developer interested in applying machine learning to your mobile apps. This book is also for those who are machine learning developers or deep learning practitioners who want to bring the power of neural networks in their iOS apps. Some exposure to machine learning concepts would be beneficial but not essential, as this book acts as a launchpad into the world of machine learning for developers. What You Will Learn ? Understand components of an ML project using algorithms, problems, and data ? Master Core ML by obtaining and importing machine learning model, and generate classes ? Prepare data for machine learning model and interpret results for optimized solutions ? Create and optimize custom layers for unsupported layers ? Apply CoreML to image and video data using CNN ? Learn the qualities of RNN to recognize sketches, and augment drawing ? Use Core ML transfer learning to execute style transfer on images In Detail Core ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine learning models and then integrate them into your iOS apps. Machine Learning with Core ML is a fun and practical guide that not only demystifies Core ML but also sheds light on machine learning. In this book, you’ll walk through realistic and interesting examples of machine learning in the context of mobile platforms (specifically iOS). You’ll learn to implement Core ML for visual-based applications using the principles of transfer learning and neural networks. Having got to grips with the basics, you’ll discover a series of seven examples, each providing a new use-case that uncovers how machine learning can be applied along with the related concepts. By the end of the book, you will have the skills required to put machine learning to work in their own applications, using the Core ML APIs Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of CoreML

Develop and deploy fully functional applications and microservices utilising Tomcat, Glassfish servers, Cloud and docker in Java EE 8 About This Book ? Explore the complete workflow of developing enterprise Java applications ? Develop microservices with Docker Container and deploy it in cloud ? Simplify Java EE application development Who This Book Is For If you are a Java developer with little or no experience in Java EE application development, or if you have experience in Java EE technology but are looking for tips to simplify and accelerate your development process, then this book is for you. What You Will Learn ? Set up Eclipse, Tomcat, and Glassfish servers for Java EE application development ? Use JSP, Servlet, JSF, and EJBs to create a user interface and write business logic ? Create Java EE database applications using JDBC and JPA ? Handle asynchronous messages using MDBs for better scalability ? Deploy and debug Java EE applications and create SOAP and REST web services ? Write unit tests and calculate code coverage ? Use Eclipse MAT (Memory Analysis Tool) to debug memory issues ? Create and deploy microservices In Detail Java EE is one of the most popular tools for enterprise application design and development. With recent changes to Java EE 8 specifications, Java EE application development has become a lot simpler with the new specifications, some of which compete with the existing specifications. This guide provides a complete overview of developing highly performant, robust and secure enterprise applications with Java EE with Eclipse. The book begins by exploring different Java EE technologies and how to use them (JSP, JSF, JPA, JDBC, EJB, and more), along with suitable technologies for different scenarios. You will learn how to set up the development environment for Java EE applications and understand Java EE specifications in detail, with an emphasis on examples. The book takes you through deployment of an application in Tomcat, GlassFish Servers, and also in the cloud. It goes beyond the basics and covers topics like debugging, testing, deployment, and securing your Java EE applications. You'll also get to know techniques to develop cloud-ready microservices in Java EE. Style and approach This guide takes a step-by-step approach to developing, testing, debugging, and troubleshooting Java EE applications, complete with examples and tips.

Understand malware analysis and its practical implementation About This Book ? Explore the key concepts of malware analysis and memory forensics using real-world examples ? Learn the art of detecting, analyzing, and investigating malware threats ? Understand adversary tactics and techniques Who This Book Is For This book is for incident responders, cyber-security investigators, system administrators, malware analyst, forensic practitioners, student, or curious security professionals interested in learning malware analysis and memory forensics. Knowledge of programming languages such as C and Python is helpful but is not mandatory. If you have written few lines of code and have a basic understanding of programming concepts, you’ll be able to get most out of this book. What You Will Learn ? Create a safe and isolated lab environment for malware analysis ? Extract the metadata associated with malware ? Determine malware's interaction with the system ? Perform code analysis using IDA Pro and x64dbg ? Reverse-engineer various malware functionalities ? Reverse engineer and decode common encoding/encryption algorithms ? Perform different code injection and hooking techniques ? Investigate and hunt malware using memory forensics In Detail Malware analysis and memory forensics are powerful analysis and investigation techniques used in reverse engineering, digital forensics, and incident response. With adversaries becoming sophisticated and carrying out advanced malware attacks on critical infrastructures, data centers, and private and public organizations, detecting, responding to, and investigating such intrusions is critical to information security professionals. Malware analysis and memory forensics have become must-have skills to fight advanced malware, targeted attacks, and security breaches. This book teaches you the concepts, techniques, and tools to understand the behavior and characteristics of malware through malware analysis. It also teaches you techniques to investigate and hunt malware using memory forensics. This book introduces you to the basics of malware analysis, and then gradually progresses into the more advanced concepts of code analysis and memory forensics. It uses real-world malware samples, infected memory images, and visual diagrams to help you gain a better understanding of the subject and to equip you with the skills required to analyze, investigate, and respond to malware-related incidents. Style and approach The book takes the reader through all the concepts, techniques and tools to understand the behavior and characteristics of malware by using malware analysis and it also teaches the techniques to investigate and hunt malware using memory forensics.

Learn how to build, test, secure, deploy, and efficiently consume services across distributed systems. About This Book ? Explore the wealth of options provided by Spring Cloud for wiring service dependencies in microservice systems. ? Create microservices utilizing Spring Cloud's Netflix OSS ? Architect your cloud-native data using Spring Cloud. Who This Book Is For This book appeals to developers keen to take advantage of Spring cloud, an open source library which helps developers quickly build distributed systems. Knowledge of Java and Spring Framework will be helpful, but no prior exposure to Spring Cloud is required. What You Will Learn ? Abstract Spring Cloud's feature set ? Create microservices utilizing Spring Cloud's Netflix OSS ? Create synchronous API microservices based on a message-driven architecture. ? Explore advanced topics such as distributed tracing, security, and contract testing. ? Manage and deploy applications on the production environment In Detail Developing, deploying, and operating cloud applications should be as easy as local applications. This should be the governing principle behind any cloud platform, library, or tool. Spring Cloud–an open-source library–makes it easy to develop JVM applications for the cloud. In this book, you will be introduced to Spring Cloud and will master its features from the application developer's point of view. This book begins by introducing you to microservices for Spring and the available feature set in Spring Cloud. You will learn to configure the Spring Cloud server and run the Eureka server to enable service registration and discovery. Then you will learn about techniques related to load balancing and circuit breaking and utilize all features of the Feign client. The book now delves into advanced topics where you will learn to implement distributed tracing solutions for Spring Cloud and build message-driven microservice architectures. Before running an application on Docker container s, you will master testing and securing techniques with Spring Cloud. Style and approach This comprehensive guide covers the advanced features of Spring Cloud and communicates them through a practical approach to explore the underlying concepts of how, when, and why to use them.

Discover how to rapidly prototype SEO-friendly web applications with Angular Universal About This Book : ? Rapidly build an application that's optimized for search performance ? Develop service workers to make your application truly progressive ? Automatically update metadata and load in content from external APIs Who This Book Is For : This book is ideal for experienced front-end developers who are looking to quickly work through an intelligent example that demonstrates all the key features of server-side development with Angular. You'll need some prior exposure to Angular, as we skim over the basics and get straight to work. What You Will Learn : ? Use the official tools provided by Angular to build an SEO-friendly application ? Create a dynamic web application that maps to current Angular best practices ? Manage your Angular applications with Angular CLI ? Implement server-side rendering for your future web application projects ? Configure service workers to automatically update your application in the background In Detail : Equip yourself with the skills required to create modern, progressive web applications that load quickly and efficiently. This fast-paced guide to server-side Angular leads you through an example application that uses Angular Universal to render application pages on the server, rather than the client. You'll learn how to serve your users views that load instantly, while reaping all the SEO benefits of improved page indexing. With differences of just 200 milliseconds in performance having a measurable impact on your users, it's more important than ever to get server-side right. Style and approach : With this book, you'll be equipped to create modern, SEO-friendly web apps with best practices using Angular CLI. This book focuses on creating a progressive web app using Angular that is optimized for search engines.

Build and deploy powerful neural network models using the latest Java deep learning libraries About This Book ? Understand DL with Java by implementing real-world projects ? Master implementations of various ANN models and build your own DL systems ? Develop applications using NLP, image classification, RL, and GPU processing Who This Book Is For If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required. What You Will Learn ? Master deep learning and neural network architectures ? Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs ? Train ML agents to learn from data using deep reinforcement learning ? Use factorization machines for advanced movie recommendations ? Train DL models on distributed GPUs for faster deep learning with Spark and DL4J ? Ease your learning experience through 69 FAQs In Detail Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems. Style and approach A unique, learn-as-you-do approach, as the reader builds on his understanding of deep learning with Java progressively with each project. This book is designed in such a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently.