.NET Design Patterns. Learn to Apply Patterns in daily development tasks under .NET Platform to take your productivity to new heights

Arun Narayanan, Praseed Pai, Shine Xavier

Knowing about design patterns enables developers to improve their code base, promoting code reuse and making their design more robust.This book focuses on the practical aspects of programming in .NET. You will learn about some of the relevant design patterns (and their application) that are most widely used. We start with classic object-oriented programming (OOP) techniques, evaluate parallel programming and concurrency models, enhance implementations by mixing OOP and functional programming, and finally to the reactive programming model where functional programming and OOP are used in synergy to write better code. Throughout this book, we’ll show you how to deal with architecture/design techniques, GoF patterns, relevant patterns from other catalogs, functional programming, and reactive programming techniques. After reading this book, you will be able to convincingly leverage these design patterns (factory pattern, builder pattern, prototype pattern, adapter pattern, facade pattern, decorator pattern, observer pattern and so on) for your programs. You will also be able to write fluid functional code in .NET that would leverage concurrency and parallelism!

Performance Testing with JMeter 3. Enhance the performance of your web application - Third Edition

Bayo Erinle

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.

Mastering Node.js. Build robust and scalable real-time server-side web applications efficiently - Second Edition

Sandro Pasquali, Kevin Faaborg

Node.js, a modern development environment that enables developers to write server- and client-side code with JavaScript, thus becoming a popular choice among developers.This book covers the features of Node that are especially helpful to developers creating highly concurrent real-time applications. It takes you on a tour of Node's innovative event non-blocking design, showing you how to build professional applications. This edition has been updated to cover the latest features of Node 9 and ES6. All code examples and demo applications have been completely rewritten using the latest techniques, introducing Promises, functional programming, async/await, and other cutting-edge patterns for writing JavaScript code. Learn how to use microservices to simplify the design and composition of distributed systems. From building serverless cloud functions to native C++ plugins, from chatbots to massively scalable SMS-driven applications, you'll be prepared for building the next generation of distributed software.By the end of this book, you'll be building better Node applications more quickly, with less code and more power, and know how to run them at scale in production environments.

Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications

Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, ...

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow.By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.

Web Development with MongoDB and Node. Build fast web applications for handling any kind of data - Third Edition

Bruno Joseph D'mello

Node.js builds fast, scalable network applications while MongoDB is the perfect fit as a high-performance, open source NoSQL database solution. The combination of these two technologies offers high performance and scalability and helps in building fast, scalable network applications. Together they provide the power for manage any form of data as well as speed of delivery.This book will help you to get these two technologies working together to build web applications quickly and easily, with effortless deployment to the cloud. You will also learn about angular 4, which consumes pure JSON APOIs from a hapi server.The book begins by setting up your development environment, running you through the steps necessary to get the main application server up-and-running. Then you will see how to use Node.js to connect to a MongoDB database and perform data manipulations.From here on, the book will take you through integration with third-party tools to interact with web apps. You will see how to use controllers and view models to generate reusable code that will reduce development time. Toward the end, the book supplies tests to properly execute your code and take your skills to the next level with the most popular frameworks for developing web applications.By the end of the book, you will have a running web application developed with MongoDB, Node.js, and some of the most powerful and popular frameworks.

Learning Malware Analysis. Explore the concepts, tools, and techniques to analyze and investigate Windows malware

Monnappa K A

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.

CMake Cookbook. Building, testing, and packaging modular software with modern CMake

Radovan Bast, Roberto Di Remigio

CMake is cross-platform, open-source software for managing the build process in a portable fashion. This book features a collection of recipes and building blocks with tips and techniques for working with CMake, CTest, CPack, and CDash.CMake Cookbook includes real-world examples in the form of recipes that cover different ways to structure, configure, build, and test small- to large-scale code projects. You will learn to use CMake's command-line tools and master modern CMake practices for configuring, building, and testing binaries and libraries. With this book, you will be able to work with external libraries and structure your own projects in a modular and reusable way. You will be well-equipped to generate native build scripts for Linux, MacOS, and Windows, simplify and refactor projects using CMake, and port projects to CMake.

Bayesian Analysis with Python. Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ - Second Edition

Osvaldo Martin

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.