Informatyka
Salil Ahuja, Swami Chandrasekaran
By adopting an SOA approach in Business Process Management (BPM), you can make your application flexible, reusable, and adaptable to new developments. The SOA approach also gives you the potential to lower costs (from reuse), and increase revenue (from adaptability and flexibility). However, integrating basic SOA constructs (such as Process, Business Services, and Components) and core building blocks of BPM (such as Process Modeling and Enterprise Service Bus) in a real-world application can be challenging.This book introduces basic concepts of Business Integration, SOA Fundamentals, and SOA Programming Model and implements them in numerous examples. It guides you to building an Order Management application from scratch using the principles of Business Process Management and Service Oriented Architecture and using WebSphere Process Server (WPS) and WebSphere Enterprise Service Bus (WESB). The various detailed aspects, features, and capabilities of the product are conveyed through examplesWe begin with essential concepts on Business Integration, SOA Fundamentals and SOA Programming Model. Then we set up the development environment to build your first Hello Process and Hello Mediation applications.Gradually, we build an SOA-based Order Management Application. We cover important aspects and functions of WPS and WESB with numerous practical examples. We show how to analyze your application's business requirements and check if an SOA approach is appropriate for your project. Then you do a top-down decomposition of your application and identify its use cases, business processes, and services. Having built the SOA Application, we introduce you to various non-functional topics, including: Administration, Governance, Management, Monitoring, and Security. We also discuss deployment topologies for WPS and WESB, performance tuning, and recommended practices.
Application Development in iOS 7
Kyle Begeman
A hybrid between informative overview and step-by-step instruction, this book is the ultimate guide to iOS 7 application development. If you are an iOS developer who is looking to understand the new features within iOS 7 and Xcode 5, then this book is for you. In order to fully understand the concepts in this book, it is recommended you have at least some experience with previous versions of iOS.
Hossam Ghareeb
After years of using Objective-C for developing apps for iOS/Mac OS, Apple now offers a new, creative, easy, and innovative programming language for application development, called Swift. Swift makes iOS application development a breeze by offering speed, security and power to your application development process. Swift is easy to learn and has awesome features such as being open source, debugging,interactive playgrounds, error handling model, and so on. Swift has simplified its memory management with Automatic Reference Counting (ARC) and it is compatible with Objective-C.This book has been created to provide you with the information and skills you need to use the new programming language Swift. The book starts with an introduction to Swift and code structure. Following this, you will use playgrounds to become familiar with the language in no time. Then the book takes you through the advanced features offered by Swift and how to use them with your old Objective-C code or projects. You will then learn to use Swift in real projects by covering APIs such as HealthKit, Metal, WatchKit, and Touch ID in each chapter. The book's easy to follow structure ensures you get the best start to developing applications with Swift.
Richard Seroter, Ewan Fairweather
Every day, architects and developers are asked to solve specific business problems in the most efficient way possible using a broad range of technologies. Packed with real-world examples of how to use the latest Microsoft technologies, this book tackles over a dozen specific use case patterns and provides an applied implementation with supporting code downloads for every chapter.In this book, we guide you through thirteen architectural patterns and provide detailed code samples for the following technologies: Windows Server AppFabric, Windows Azure Platform AppFabric, SQL Server (including Integration Services, Service Broker, and StreamInsight), BizTalk Server, Windows Communication Foundation (WCF), and Windows Workflow Foundation (WF). This book brings together – and simplifies – the information and methodology you need to make the right architectural decisions and use a broad range of the Microsoft platform to meet your requirements. Throughout the book, we will follow a consistent architectural decision framework which considers key business, organizational, and technology factors.The book is broken up into four sections. First, we define the techniques and methodologies used to make architectural decisions throughout the book. In Part I, we provide a set of primers designed to get you up to speed with each of the technologies demonstrated in the book. Part II looks at messaging patterns and includes use cases which highlight content-based routing, workflow, publish/subscribe, and distributed messaging. Part III digs into data processing patterns and looks at bulk data processing, complex events, multi-master synchronization, and more. Finally, Part IV covers performance-related patterns including low latency, failover to the cloud, and reference data caching.
Sofía De Jesús, Dayrene Martinez
Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
Alex Galea, Luis Capelo
Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.
David S. Jordan
Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.
Mani Khanuja, Farooq Sabir , Shreyas Subramanian,...
Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.