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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 out
scenarios and code samples that can be used in many real-world
situations. What You Will Learn Create a machine learning model
using only the C# language Build confidence in your understanding
of machine learning algorithms Painlessly implement algorithms
Begin using the ML.NET library software Recognize the many
opportunities to utilize ML.NET to your advantage Apply and reuse
code samples from the book Utilize the bonus algorithm selection
quick references available online Who This Book Is For Developers
who want to learn how to use and apply machine learning to enrich
their 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 Learn
Understand the Roslyn Syntax API Use Data Visualization techniques
to assist code analysis process visually Code health monitoring
matrices (from the standard of Code Query Language) Code mining
techniques to identify design patterns used in source code Code
forensics techniques to identify probable author of a given source
code Techniques to identify duplicate/near duplicate code Who This
Book is For .NET Software Developers and Architects
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 operators Text processing Loop refactoring Monitoring code
health Reactive Extensions (Rx.NET) Building domain-specific
languages Using 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.
Tap into bi-directional integration and analytics with SAP
Manufacturing Integration and Intelligence. With this bestselling
guide, get step-by-step instructions for configuring SAP MII,
managing external data connections, developing composite
applications, and more. Dive into business logic service
transactions and use visualization services such as i5 display
templates and web reports. Run SAP Overall Equipment Effectiveness
Management by mastering activities, dashboards, downtime
maintenance, audit log checks, and shop-floor user interfaces. Your
nitty-gritty guide to SAP MII is here! Highlights include: 1) Data
connections and management 2) Message servers 3) Composite
applications 4) Manufacturing data objects 5) Business logic
service transactions 6) Visualization services 7) Plant information
catalog (PIC) 8) SAP Plant Connectivity 9) SAP Overall Equipment
Effectiveness Management
Get up and running with machine learning with F# in a fun and
functional way About This Book * Design algorithms in F# to tackle
complex computing problems * Be a proficient F# data scientist
using this simple-to-follow guide * Solve real-world, data-related
problems with robust statistical models, built for a range of
datasets Who This Book Is For If you are a C# or an F# developer
who now wants to explore the area of machine learning, then this
book is for you. Familiarity with theoretical concepts and notation
of mathematics and statistics would be an added advantage. What You
Will Learn * Use F# to find patterns through raw data * Build a set
of classification systems using Accord.NET, Weka, and F# * Run
machine learning jobs on the Cloud with MBrace * Perform
mathematical operations on matrices and vectors using Math.NET *
Use a recommender system for your own problem domain * Identify
tourist spots across the globe using inputs from the user with
decision tree algorithms In Detail The F# functional programming
language enables developers to write simple code to solve complex
problems. With F#, developers create consistent and predictable
programs that are easier to test and reuse, simpler to parallelize,
and are less prone to bugs. If you want to learn how to use F# to
build machine learning systems, then this is the book you want.
Starting with an introduction to the several categories on machine
learning, you will quickly learn to implement time-tested,
supervised learning algorithms. You will gradually move on to
solving problems on predicting housing pricing using Regression
Analysis. You will then learn to use Accord.NET to implement SVM
techniques and clustering. You will also learn to build a
recommender system for your e-commerce site from scratch. Finally,
you will dive into advanced topics such as implementing neural
network algorithms while performing sentiment analysis on your
data. Style and approach This book is a fast-paced tutorial guide
that uses hands-on examples to explain real-world applications of
machine learning. Using practical examples, the book will explore
several machine learning techniques and also describe how you can
use F# to build machine learning systems.
This is a concise, practical guide that will help you learn
Generics in .NET, with lots of real world and fun-to-build examples
and clear explanations. It is packed with screenshots to aid your
understanding of the process. This book is aimed at beginners in
Generics. It assumes some working knowledge of C# , but it isn't
mandatory. The following would get the most use out of the book:
Newbie C# developers struggling with Generics. Experienced C++ and
Java Programmers who are migrating to C# and looking for an
alternative to other generic frameworks like STL and JCF would find
this book handy. Managers who want to know what Generics is and how
to put it to good use. Architects will find the benchmarking
extremely useful, because it's the first of its kind across a
framework of several collections.
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Nadine Gordimer
Paperback
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R398
R330
Discovery Miles 3 300
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