Machine Learning Projects for .NET Developers / by Mathias Brandewinder

By:

Brandewinder, Mathias [aut]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink BücherPublisher: [New York, NY] : Apress, 2015Description: Online-Ressource (XIX, 300 p. 84 illus, online resource)ISBN:

9781430267669

Subject(s):

Additional physical formats: 9781430267676 | Druckausg.: 978-143-026-767-6 | Erscheint auch als: Machine learning projects for .Net developers. Druck-Ausgabe New York, NY : Apress; Springer, 2015. XIX, 275 S.RVK: RVK: ST 253 | ST 232LOC classification:

QA75.5-76.95

DOI: DOI: 10.1007/978-1-4302-6766-9Online resources:

Summary: Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You’ll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET. If you’re new to F#, this book will give you everything you need to get started. If you’re already familiar with F#, this is your chance to put the language into action in an exciting new context. In a series of fascinating projects, you’ll learn how to: Build an optical character recognition (OCR) system from scratch Code a spam filter that learns by example Use F#’s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language) Transform your data into informative features, and use them to make accurate predictions Find patterns in data when you don’t know what you’re looking for Predict numerical values using regression models Implement an intelligent game that learns how to play from experience Along the way, you’ll learn fundamental ideas that can be applied in all kinds of real-world contexts and industries, from advertising to finance, medicine, and scientific research. While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for youPPN: PPN: 1657482197Package identifier: Produktsigel: ZDB-2-CWD

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