Isbn: 9781430267676 - machine learning projects for .net developers (19 risultati)

Paperback. Condizione: Fair. 1st ed. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

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Condizione: New. In English.

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Condizione: New. pp. 320.

Paperback. Condizione: Brand New. 320 pages. 10.00x7.25x0.75 inches. In Stock.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 300 pp. Englisch.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Mathias Brandewinder is a Microsoft MVP for F# based in San Francisco, California. An unashamed math geek, he became interested early on in building models to help others make better decisions using data. He collected graduate degrees in Business, Economics.…

Condizione: New. Print on Demand pp. 320.

Condizione: New. PRINT ON DEMAND pp. 320.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 scratchCode a spam filter that learns by exampleUse F#'s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language)Transform your data intoinformative features, and use them to make accurate predictionsFind patterns in data when you don't know what you're looking forPredict numerical values using regression modelsImplement an intelligent game that learns how to play from experienceAlong 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 you.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 300 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 scratchCode a spam filter that learns by exampleUse F# s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language)Transform your data intoinformative features, and use them to make accurate predictionsFind patterns in data when you don t know what you re looking forPredict numerical values using regression modelsImplement an intelligent game that learns how to play from experienceAlong 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 you.…