Sudipta Mukherjee - Böcker

LINQ represents a paradigm shift for developers used to an imperative/object oriented programming style, because LINQ draws on functional programming principles. Thinking in LINQ addresses the differences between these two by providing a set of succinct recipes arranged in several groups, including: Basic and extended LINQ operatorsText processing Loop refactoring Monitoring code health Reactive Extensions (Rx.NET)Building domain-specific languagesUsing the familiar "recipes" approach, Thinking in LINQ shows you how to approach building LINQ-based solutions, how such solutions are different from what you already know, and why they’re better. The recipes cover a wide range of real-world problems, from using LINQ to replace existing loops, to writing your own Swype-like keyboard entry routines, to finding duplicate files on your hard drive. The goal of these recipes is to get you "thinking in LINQ," so you can use the techniques in your own code to write more efficient and concise data-intensive applications.

Learn how to build an interactive source code analytics system using Roslyn and JavaScript. This concise 150 page book will help you create and use practical code analysis tools utilizing the new features of Microsoft’s Roslyn compiler to understand the health of your code and identify parts of the code for refactoring. Source code is one of the biggest assets of a software company. However if not maintained well, it can become a big liability. As source code becomes larger. more complex and accessed via the cloud, maintaining code quality becomes even more challenging. The author provides straightforward tools and advice on how to manage code quality in this new environment. Roslyn exposes a set of APIs which allow developers to parse their C# and VB.NET code and drastically lower the barrier to entry for Meta programming in .NET. Roslyn has a dedicated set of APIs for creating custom refactoring for integrating with Visual Studio. This title will show readers how to use Roslyn along with industry standard JavaScript visualization APIs like HighCharts, D3.js etc to create a scalable and highly responsive source code analytics system. What You Will LearnUnderstand the Roslyn Syntax APIUse Data Visualization techniques to assist code analysis process visuallyCode health monitoring matrices (from the standard of Code Query Language)Code mining techniques to identify design patterns used in source codeCode forensics techniques to identify probable author of a given source codeTechniques to identify duplicate/near duplicate codeWho This Book is For.NET Software Developers and Architects

Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible.Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary “plumbing” that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not. Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try outscenarios and code samples that can be used in many real-world situations.What You Will LearnCreate a machine learning model using only the C# languageBuild confidence in your understanding of machine learning algorithms                                   Painlessly implement algorithms                                                                                 Begin using the ML.NET library softwareRecognize the many opportunities to utilize ML.NET to your advantageApply and reuse code samples from the bookUtilize the bonus algorithm selection quick references available onlineWho This Book Is ForDevelopers who want to learn how to use and apply machine learning to enrich their applications

This book highlights the enormous potential of F#—in comparison to other programming languages—to be used in scientific computing. It presents solutions of diverse real-life problems by using the combination of applied mathematics and F#. The book begins by introducing different generic collections offered by F# and goes on to present their usages in various areas of applied mathematics such as vectors and matrices, descriptive statistics, set theory, probability and calculus. Empowering users to tackle complex computing problems by using succinct yet robust code, F# is  very well positioned to be used for applied mathematics; making this book all the more important for interested readers in this area. The book uses Plotly.NET to create interactive, publication-quality charts and visualizes vector fields, Jacobians, matrices as heatmaps, and probability distributions and KDEs, ordinary differential equations solutions and phase plots. It uses AngouriMath for symbolic differentiation, simplification, and algebra. It also shows how to blend symbolic math with numerical methods like Trapezoidal and Simpson’s rule, Runge–Kutta methods and Gaussian quadrature. The book includes several real-life case studies as examples such as soccer player analytics, housing price analysis, bungee jumper physics simulation, predator–prey ecosystem model and credit score evaluation.