Machine Learning Projects for .NET Developers

Paperback Engels 2015 2014e druk 9781430267676
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

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 into informative 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 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 you.

Specificaties

ISBN13:9781430267676
Taal:Engels
Bindwijze:paperback
Aantal pagina's:320
Uitgever:Apress
Druk:2014

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Inhoudsopgave

<p>Chapter 1: 256 Shades of Gray: Building A Program to Automatically Recognize Images of Numbers</p><p>Chapter 2: Spam or Ham? Detecting Spam in Text Using Bayes' Theorem</p><p>Chapter 3: The Joy of Type Providers: Finding and Preparing Data, From Anywhere</p><p>Chapter 4: Of Bikes and Men: Fitting a Regression Model to Data with Gradient Descent</p><p>Chapter 5: You Are Not An Unique Snowflake: Detecting Patterns with Clustering and Principle Component Analysis</p><p>Chapter 6: Trees and Forests: Making Predictions from Incomplete Data </p><p>Chapter 7: A Strange Game: Learning From Experience with Reinforcement Learning</p><p>Chapter 8: Digits, Revisited: Optimizing and Scaling Your Algorithm Code</p><p>Chapter 9: Conclusion</p><p></p><p></p>

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        Machine Learning Projects for .NET Developers