Building Mobile Applications Using Kendo UI Mobile and ASP.NET Web API. Confident of your web application skills but not yet au fait with mobile development? Well this book helps you use the Kendo UI for a painless introduction. Practical tasks and clear instructions make learning a breeze

Ragini Kumbhat Bhandari, Nishanth Nair, Ragini Bhandari

With the world becoming more mobile, there is a growing need for mobile websites and applications. Building these from scratch is not a simple process. Kendo UI Mobile makes building websites and applications easier than ever before. Build applications for phones and tablets in no time at all and provide your user with a native look and feel.Building Mobile Applications Using Kendo UI Mobile and ASP.NET Web API helps you to understand the concept of building mobile applications using HTML5 based frameworks, specifically Kendo UI Mobile. It teaches you in a simple step-by-step manner how to create a service backend layer using ASP.NET Web API and how to integrate it with your front end mobile application, which is the missing piece of the puzzle for most developers who are new to mobile applications development. Learn everything from the basics of HTML5 to design and development of a mobile application using Kendo UI Mobile and ASP.NET Web API. Integrate them in the right way using extensible and maintainable JavaScript code.Starting with the basics of the Kendo UI platform, learn how to build a real world mobile application from scratch. You will explore the Kendo UI framework elements and integrate the sample mobile application with the ASP.NET Web API service. One of the most important things that you will learn from this book is how to organize your code using the JavaScript Revealing Module Pattern. You will also take a journey through Kendo UI Mobile widgets with lots of code samples hosted in jsFiddle. At the end of this book, you will complete the integration of the sample application and master fixing real world problems utilizing your newly acquired professional techniques that will save you time and effort.Building Mobile Applications Using Kendo UI Mobile and ASP.NET Web API will help you improve your mobile application development skills using hands-on examples, and will help you address the common problems faced by beginners as well as experienced web programmers. 

Effective Concurrency in Go. Develop, analyze, and troubleshoot high performance concurrent applications with ease

Burak Serdar

The Go language has been gaining momentum due to its treatment of concurrency as a core language feature, making concurrent programming more accessible than ever. However, concurrency is still an inherently difficult skill to master, since it requires the development of the right mindset to decompose problems into concurrent components correctly. This book will guide you in deepening your understanding of concurrency and show you how to make the most of its advantages.You’ll start by learning what guarantees are offered by the language when running concurrent programs. Through multiple examples, you will see how to use this information to develop concurrent algorithms that run without data races and complete successfully. You’ll also find out all you need to know about multiple common concurrency patterns, such as worker pools, asynchronous pipelines, fan-in/fan-out, scheduling periodic or future tasks, and error and panic handling in goroutines.The central theme of this book is to give you, the developer, an understanding of why concurrent programs behave the way they do, and how they can be used to build correct programs that work the same way in all platforms.By the time you finish the final chapter, you’ll be able to develop, analyze, and troubleshoot concurrent algorithms written in Go.

Hands-On Data Preprocessing in Python. Learn how to effectively prepare data for successful data analytics

Roy Jafari

Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who’s developed college-level courses on data preprocessing and related subjects. With this book, you’ll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you’ll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data.By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.

AI Crash Course. A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python

Hadelin de Ponteves

Welcome to the Robot World … and start building intelligent software now!Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch.AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination.

Microsoft SQL Server 2012 with Hadoop. Getting SQL Server talking to Hadoop is a smooth process when you follow this tutorial. Learn all the tools and techniques you need integrate the data and then extract powerful business insights from the merged result

Debarchan Sarkar

With the explosion of data, the open source Apache Hadoop ecosystem is gaining traction, thanks to its huge ecosystem that has arisen around the core functionalities of its distributed file system (HDFS) and Map Reduce. As of today, being able to have SQL Server talking to Hadoop has become increasingly important because the two are indeed complementary. While petabytes of unstructured data can be stored in Hadoop taking hours to be queried, terabytes of structured data can be stored in SQL Server 2012 and queried in seconds. This leads to the need to transfer and integrate data between Hadoop and SQL Server.Microsoft SQL Server 2012 with Hadoop is aimed at SQL Server developers. It will quickly show you how to get Hadoop activated on SQL Server 2012 (it ships with this version). Once this is done, the book will focus on how to manage big data with Hadoop and use Hadoop Hive to query the data. It will also cover topics such as using in-memory functions by SQL Server and using tools for BI with big data.Microsoft SQL Server 2012 with Hadoop focuses on data integration techniques between relational (SQL Server 2012) and non-relational (Hadoop) worlds. It will walk you through different tools for the bi-directional movement of data with practical examples.You will learn to use open source connectors like SQOOP to import and export data between SQL Server 2012 and Hadoop, and to work with leading in-memory BI tools to create ETL solutions using the Hive ODBC driver for developing your data movement projects. Finally, this book will give you a glimpse of the present day self-service BI tools such as Excel and PowerView to consume Hadoop data and provide powerful insights on the data.

Engineering Data Mesh in Azure Cloud. Implement data mesh using Microsoft Azure's Cloud Adoption Framework

Aniruddha Deswandikar

Decentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help.The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you’ll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud.The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI).By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.

Microsoft Hyper-V PowerShell Automation. Manage, automate, and streamline your Hyper-V environment effectively with advanced PowerShell cmdlets

Vinith Menon

Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Aditya Bhattacharya

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.